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z5^&s)7H^HuK^Ecg;=0Ym*3M4JlOatV7`gtX@dl+ajnZWDc;NUQg2#h;zbg3bY&s5B zzx81aU58t6PXxhC03PTS2WY5BX~c_5f(damZV5Rl=r+)#XrB1ld=IG*Vb4a!^@+ywVQ>LS50pl@yDDDyU${zhi9i5nH2U?hxX5JGt-J_!l&5?ou z^&6Pg_rPm&hBTR9y?V8y)@P$}qL~f7r8>%^J^%RNGH`Fe5^{il148ed{2JB?8ct2a z3zmL^;c!94)KMUeh+^rRH-LU1)Band;oqAM|C{&82UO|_U&a&!f8xQE5JFY!R?*Ez G&;A2V>4kX! diff --git a/main/_modules/highway_env/__init__/index.html b/main/_modules/highway_env/__init__/index.html index 03f0ee27c..4f3578e75 100644 --- a/main/_modules/highway_env/__init__/index.html +++ b/main/_modules/highway_env/__init__/index.html @@ -291,10 +291,22 @@

Source code for highway_env.__init__

-# Hide pygame support prompt
-import os
-os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
+import os
+import sys
 
+__version__ = "1.8.2"
+
+try:
+    from farama_notifications import notifications
+
+    if "highway_env" in notifications and __version__ in notifications["gymnasium"]:
+        print(notifications["highway_env"][__version__], file=sys.stderr)
+
+except Exception:  # nosec
+    pass
+
+# Hide pygame support prompt
+os.environ["PYGAME_HIDE_SUPPORT_PROMPT"] = "1"
 
 from gymnasium.envs.registration import register
 
@@ -306,96 +318,89 @@ 
Source code for highway_env.__init__
 
     # exit_env.py
     register(
-        id='exit-v0',
-        entry_point='highway_env.envs:ExitEnv',
+        id="exit-v0",
+        entry_point="highway_env.envs:ExitEnv",
     )
 
     # highway_env.py
     register(
-        id='highway-v0',
-        entry_point='highway_env.envs:HighwayEnv',
+        id="highway-v0",
+        entry_point="highway_env.envs:HighwayEnv",
     )
 
     register(
-        id='highway-fast-v0',
-        entry_point='highway_env.envs:HighwayEnvFast',
+        id="highway-fast-v0",
+        entry_point="highway_env.envs:HighwayEnvFast",
     )
 
     # intersection_env.py
     register(
-        id='intersection-v0',
-        entry_point='highway_env.envs:IntersectionEnv',
+        id="intersection-v0",
+        entry_point="highway_env.envs:IntersectionEnv",
     )
 
     register(
-        id='intersection-v1',
-        entry_point='highway_env.envs:ContinuousIntersectionEnv',
+        id="intersection-v1",
+        entry_point="highway_env.envs:ContinuousIntersectionEnv",
     )
 
     register(
-        id='intersection-multi-agent-v0',
-        entry_point='highway_env.envs:MultiAgentIntersectionEnv',
+        id="intersection-multi-agent-v0",
+        entry_point="highway_env.envs:MultiAgentIntersectionEnv",
     )
 
     register(
-        id='intersection-multi-agent-v1',
-        entry_point='highway_env.envs:TupleMultiAgentIntersectionEnv',
+        id="intersection-multi-agent-v1",
+        entry_point="highway_env.envs:TupleMultiAgentIntersectionEnv",
     )
 
     # lane_keeping_env.py
     register(
-        id='lane-keeping-v0',
-        entry_point='highway_env.envs:LaneKeepingEnv',
-        max_episode_steps=200
+        id="lane-keeping-v0",
+        entry_point="highway_env.envs:LaneKeepingEnv",
+        max_episode_steps=200,
     )
 
     # merge_env.py
     register(
-        id='merge-v0',
-        entry_point='highway_env.envs:MergeEnv',
+        id="merge-v0",
+        entry_point="highway_env.envs:MergeEnv",
     )
 
     # parking_env.py
     register(
-        id='parking-v0',
-        entry_point='highway_env.envs:ParkingEnv',
+        id="parking-v0",
+        entry_point="highway_env.envs:ParkingEnv",
     )
 
     register(
-        id='parking-ActionRepeat-v0',
-        entry_point='highway_env.envs:ParkingEnvActionRepeat'
+        id="parking-ActionRepeat-v0",
+        entry_point="highway_env.envs:ParkingEnvActionRepeat",
     )
 
     register(
-        id='parking-parked-v0',
-        entry_point='highway_env.envs:ParkingEnvParkedVehicles'
+        id="parking-parked-v0", entry_point="highway_env.envs:ParkingEnvParkedVehicles"
     )
 
     # racetrack_env.py
     register(
-        id='racetrack-v0',
-        entry_point='highway_env.envs:RacetrackEnv',
+        id="racetrack-v0",
+        entry_point="highway_env.envs:RacetrackEnv",
     )
 
     # roundabout_env.py
     register(
-        id='roundabout-v0',
-        entry_point='highway_env.envs:RoundaboutEnv',
+        id="roundabout-v0",
+        entry_point="highway_env.envs:RoundaboutEnv",
     )
 
     # two_way_env.py
     register(
-        id='two-way-v0',
-        entry_point='highway_env.envs:TwoWayEnv',
-        max_episode_steps=15
+        id="two-way-v0", entry_point="highway_env.envs:TwoWayEnv", max_episode_steps=15
     )
 
     # u_turn_env.py
-    register(
-        id='u-turn-v0',
-        entry_point='highway_env.envs:UTurnEnv'
-    )

-
+    register(id="u-turn-v0", entry_point="highway_env.envs:UTurnEnv")
diff --git a/main/_modules/highway_env/envs/common/abstract/index.html b/main/_modules/highway_env/envs/common/abstract/index.html index 7fc38100e..7f4ec3638 100644 --- a/main/_modules/highway_env/envs/common/abstract/index.html +++ b/main/_modules/highway_env/envs/common/abstract/index.html @@ -293,20 +293,19 @@

Source code for highway_env.envs.common.abstract

 import copy
 import os
-from typing import List, Tuple, Optional, Callable, TypeVar, Generic, Union, Dict, Text
+from typing import Dict, List, Optional, Text, Tuple, TypeVar
+
 import gymnasium as gym
+import numpy as np
 from gymnasium import Wrapper
 from gymnasium.wrappers import RecordVideo
-from gymnasium.utils import seeding
-import numpy as np
 
 from highway_env import utils
-from highway_env.envs.common.action import action_factory, Action, DiscreteMetaAction, ActionType
-from highway_env.envs.common.observation import observation_factory, ObservationType
+from highway_env.envs.common.action import Action, ActionType, action_factory
 from highway_env.envs.common.finite_mdp import finite_mdp
 from highway_env.envs.common.graphics import EnvViewer
-from highway_env.vehicle.behavior import IDMVehicle, LinearVehicle
-from highway_env.vehicle.controller import MDPVehicle
+from highway_env.envs.common.observation import ObservationType, observation_factory
+from highway_env.vehicle.behavior import IDMVehicle
 from highway_env.vehicle.kinematics import Vehicle
 
 Observation = TypeVar("Observation")
@@ -323,11 +322,12 @@ 
Source code for highway_env.envs.common.abstract

     speed. The action space is fixed, but the observation space and reward function must be defined in the
     environment implementations.
     """
+
     observation_type: ObservationType
     action_type: ActionType
     _record_video_wrapper: Optional[RecordVideo]
     metadata = {
-        'render_modes': ['human', 'rgb_array'],
+        "render_modes": ["human", "rgb_array"],
     }
 
     PERCEPTION_DISTANCE = 5.0 * Vehicle.MAX_SPEED
@@ -386,12 +386,8 @@ 
Source code for highway_env.envs.common.abstract

         :return: a configuration dict
         """
         return {
-            "observation": {
-                "type": "Kinematics"
-            },
-            "action": {
-                "type": "DiscreteMetaAction"
-            },
+            "observation": {"type": "Kinematics"},
+            "action": {"type": "DiscreteMetaAction"},
             "simulation_frequency": 15,  # [Hz]
             "policy_frequency": 1,  # [Hz]
             "other_vehicles_type": "highway_env.vehicle.behavior.IDMVehicle",
@@ -403,7 +399,7 @@ 
Source code for highway_env.envs.common.abstract

             "render_agent": True,
             "offscreen_rendering": os.environ.get("OFFSCREEN_RENDERING", "0") == "1",
             "manual_control": False,
-            "real_time_rendering": False
+            "real_time_rendering": False,
         }

 
 
@@ -412,9 +408,12 @@ 
Source code for highway_env.envs.common.abstract

             self.config.update(config)
 
     def update_metadata(self, video_real_time_ratio=2):
-        frames_freq = self.config["simulation_frequency"] \
-            if self._record_video_wrapper else self.config["policy_frequency"]
-        self.metadata['render_fps'] = video_real_time_ratio * frames_freq
+        frames_freq = (
+            self.config["simulation_frequency"]
+            if self._record_video_wrapper
+            else self.config["policy_frequency"]
+        )
+        self.metadata["render_fps"] = video_real_time_ratio * frames_freq
 
 

 [docs]
@@ -501,10 +500,11 @@ 
Source code for highway_env.envs.common.abstract

 
 

 [docs]
-    def reset(self,
-              *,
-              seed: Optional[int] = None,
-              options: Optional[dict] = None,
+    def reset(
+        self,
+        *,
+        seed: Optional[int] = None,
+        options: Optional[dict] = None,
     ) -> Tuple[Observation, dict]:
         """
         Reset the environment to it's initial configuration
@@ -524,7 +524,7 @@ 
Source code for highway_env.envs.common.abstract

         self.define_spaces()  # Second, to link the obs and actions to the vehicles once the scene is created
         obs = self.observation_type.observe()
         info = self._info(obs, action=self.action_space.sample())
-        if self.render_mode == 'human':
+        if self.render_mode == "human":
             self.render()
         return obs, info

 
@@ -553,7 +553,9 @@ 
Source code for highway_env.envs.common.abstract

         :return: a tuple (observation, reward, terminated, truncated, info)
         """
         if self.road is None or self.vehicle is None:
-            raise NotImplementedError("The road and vehicle must be initialized in the environment implementation")
+            raise NotImplementedError(
+                "The road and vehicle must be initialized in the environment implementation"
+            )
 
         self.time += 1 / self.config["policy_frequency"]
         self._simulate(action)
@@ -563,7 +565,7 @@ 
Source code for highway_env.envs.common.abstract

         terminated = self._is_terminated()
         truncated = self._is_truncated()
         info = self._info(obs, action)
-        if self.render_mode == 'human':
+        if self.render_mode == "human":
             self.render()
 
         return obs, reward, terminated, truncated, info

@@ -573,12 +575,21 @@ 
Source code for highway_env.envs.common.abstract

 [docs]
     def _simulate(self, action: Optional[Action] = None) -> None:
         """Perform several steps of simulation with constant action."""
-        frames = int(self.config["simulation_frequency"] // self.config["policy_frequency"])
+        frames = int(
+            self.config["simulation_frequency"] // self.config["policy_frequency"]
+        )
         for frame in range(frames):
             # Forward action to the vehicle
-            if action is not None \
-                    and not self.config["manual_control"] \
-                    and self.steps % int(self.config["simulation_frequency"] // self.config["policy_frequency"]) == 0:
+            if (
+                action is not None
+                and not self.config["manual_control"]
+                and self.steps
+                % int(
+                    self.config["simulation_frequency"]
+                    // self.config["policy_frequency"]
+                )
+                == 0
+            ):
                 self.action_type.act(action)
 
             self.road.act()
@@ -587,7 +598,9 @@ 
Source code for highway_env.envs.common.abstract

 
             # Automatically render intermediate simulation steps if a viewer has been launched
             # Ignored if the rendering is done offscreen
-            if frame < frames - 1:  # Last frame will be rendered through env.render() as usual
+            if (
+                frame < frames - 1
+            ):  # Last frame will be rendered through env.render() as usual
                 self._automatic_rendering()
 
         self.enable_auto_render = False

@@ -618,7 +631,7 @@ 
Source code for highway_env.envs.common.abstract

 
         if not self.viewer.offscreen:
             self.viewer.handle_events()
-        if self.render_mode == 'rgb_array':
+        if self.render_mode == "rgb_array":
             image = self.viewer.get_image()
             return image

 
@@ -654,7 +667,6 @@ 
Source code for highway_env.envs.common.abstract

         If a RecordVideo wrapper has been set, use it to capture intermediate frames.
         """
         if self.viewer is not None and self.enable_auto_render:
-
             if self._record_video_wrapper and self._record_video_wrapper.video_recorder:
                 self._record_video_wrapper.video_recorder.capture_frame()
             else:
@@ -663,7 +675,7 @@ 
Source code for highway_env.envs.common.abstract

 
 

 [docs]
-    def simplify(self) -> 'AbstractEnv':
+    def simplify(self) -> "AbstractEnv":
         """
         Return a simplified copy of the environment where distant vehicles have been removed from the road.
 
@@ -672,15 +684,18 @@ 
Source code for highway_env.envs.common.abstract

         :return: a simplified environment state
         """
         state_copy = copy.deepcopy(self)
-        state_copy.road.vehicles = [state_copy.vehicle] + state_copy.road.close_vehicles_to(
-            state_copy.vehicle, self.PERCEPTION_DISTANCE)
+        state_copy.road.vehicles = [
+            state_copy.vehicle
+        ] + state_copy.road.close_vehicles_to(
+            state_copy.vehicle, self.PERCEPTION_DISTANCE
+        )
 
         return state_copy

 
 
 

 [docs]
-    def change_vehicles(self, vehicle_class_path: str) -> 'AbstractEnv':
+    def change_vehicles(self, vehicle_class_path: str) -> "AbstractEnv":
         """
         Change the type of all vehicles on the road
 
@@ -698,7 +713,7 @@ 
Source code for highway_env.envs.common.abstract

         return env_copy

 
 
-    def set_preferred_lane(self, preferred_lane: int = None) -> 'AbstractEnv':
+    def set_preferred_lane(self, preferred_lane: int = None) -> "AbstractEnv":
         env_copy = copy.deepcopy(self)
         if preferred_lane:
             for v in env_copy.road.vehicles:
@@ -708,14 +723,14 @@ 
Source code for highway_env.envs.common.abstract

                     v.LANE_CHANGE_MAX_BRAKING_IMPOSED = 1000
         return env_copy
 
-    def set_route_at_intersection(self, _to: str) -> 'AbstractEnv':
+    def set_route_at_intersection(self, _to: str) -> "AbstractEnv":
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
             if isinstance(v, IDMVehicle):
                 v.set_route_at_intersection(_to)
         return env_copy
 
-    def set_vehicle_field(self, args: Tuple[str, object]) -> 'AbstractEnv':
+    def set_vehicle_field(self, args: Tuple[str, object]) -> "AbstractEnv":
         field, value = args
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
@@ -723,7 +738,7 @@ 
Source code for highway_env.envs.common.abstract

                 setattr(v, field, value)
         return env_copy
 
-    def call_vehicle_method(self, args: Tuple[str, Tuple[object]]) -> 'AbstractEnv':
+    def call_vehicle_method(self, args: Tuple[str, Tuple[object]]) -> "AbstractEnv":
         method, method_args = args
         env_copy = copy.deepcopy(self)
         for i, v in enumerate(env_copy.road.vehicles):
@@ -731,7 +746,7 @@ 
Source code for highway_env.envs.common.abstract

                 env_copy.road.vehicles[i] = getattr(v, method)(*method_args)
         return env_copy
 
-    def randomize_behavior(self) -> 'AbstractEnv':
+    def randomize_behavior(self) -> "AbstractEnv":
         env_copy = copy.deepcopy(self)
         for v in env_copy.road.vehicles:
             if isinstance(v, IDMVehicle):
@@ -739,7 +754,7 @@ 
Source code for highway_env.envs.common.abstract

         return env_copy
 
     def to_finite_mdp(self):
-        return finite_mdp(self, time_quantization=1/self.config["policy_frequency"])
+        return finite_mdp(self, time_quantization=1 / self.config["policy_frequency"])
 
     def __deepcopy__(self, memo):
         """Perform a deep copy but without copying the environment viewer."""
@@ -747,7 +762,7 @@ 
Source code for highway_env.envs.common.abstract

         result = cls.__new__(cls)
         memo[id(self)] = result
         for k, v in self.__dict__.items():
-            if k not in ['viewer', '_record_video_wrapper']:
+            if k not in ["viewer", "_record_video_wrapper"]:
                 setattr(result, k, copy.deepcopy(v, memo))
             else:
                 setattr(result, k, None)
diff --git a/main/_modules/highway_env/envs/common/action/index.html b/main/_modules/highway_env/envs/common/action/index.html
index 06f554b63..ed9b9c138 100644
--- a/main/_modules/highway_env/envs/common/action/index.html
+++ b/main/_modules/highway_env/envs/common/action/index.html
@@ -293,16 +293,16 @@
             
Source code for highway_env.envs.common.action
 import functools
 import itertools
-from typing import TYPE_CHECKING, Optional, Union, Tuple, Callable, List
-from gymnasium import spaces
+from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
+
 import numpy as np
+from gymnasium import spaces
 
 from highway_env import utils
 from highway_env.utils import Vector
-from highway_env.vehicle.behavior import IDMVehicle
+from highway_env.vehicle.controller import MDPVehicle
 from highway_env.vehicle.dynamics import BicycleVehicle
 from highway_env.vehicle.kinematics import Vehicle
-from highway_env.vehicle.controller import MDPVehicle
 
 if TYPE_CHECKING:
     from highway_env.envs.common.abstract import AbstractEnv
@@ -316,7 +316,7 @@ 
Source code for highway_env.envs.common.action
    """A type of action specifies its definition space, and how actions are executed in the environment"""
 
-    def __init__(self, env: 'AbstractEnv', **kwargs) -> None:
+    def __init__(self, env: "AbstractEnv", **kwargs) -> None:
         self.env = env
         self.__controlled_vehicle = None
 
@@ -391,16 +391,18 @@ 
Source code for highway_env.envs.common.action
STEERING_RANGE = (-np.pi / 4, np.pi / 4)
     """Steering angle range: [-x, x], in rad."""
 
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 acceleration_range: Optional[Tuple[float, float]] = None,
-                 steering_range: Optional[Tuple[float, float]] = None,
-                 speed_range: Optional[Tuple[float, float]] = None,
-                 longitudinal: bool = True,
-                 lateral: bool = True,
-                 dynamical: bool = False,
-                 clip: bool = True,
-                 **kwargs) -> None:
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        acceleration_range: Optional[Tuple[float, float]] = None,
+        steering_range: Optional[Tuple[float, float]] = None,
+        speed_range: Optional[Tuple[float, float]] = None,
+        longitudinal: bool = True,
+        lateral: bool = True,
+        dynamical: bool = False,
+        clip: bool = True,
+        **kwargs
+    ) -> None:
         """
         Create a continuous action space.
 
@@ -414,13 +416,17 @@ 
Source code for highway_env.envs.common.action
        :param clip: clip action to the defined range
         """
         super().__init__(env)
-        self.acceleration_range = acceleration_range if acceleration_range else self.ACCELERATION_RANGE
+        self.acceleration_range = (
+            acceleration_range if acceleration_range else self.ACCELERATION_RANGE
+        )
         self.steering_range = steering_range if steering_range else self.STEERING_RANGE
         self.speed_range = speed_range
         self.lateral = lateral
         self.longitudinal = longitudinal
         if not self.lateral and not self.longitudinal:
-            raise ValueError("Either longitudinal and/or lateral control must be enabled")
+            raise ValueError(
+                "Either longitudinal and/or lateral control must be enabled"
+            )
         self.dynamical = dynamical
         self.clip = clip
         self.size = 2 if self.lateral and self.longitudinal else 1
@@ -429,7 +435,7 @@ 
Source code for highway_env.envs.common.action

 [docs]
     def space(self) -> spaces.Box:
-        return spaces.Box(-1., 1., shape=(self.size,), dtype=np.float32)

+        return spaces.Box(-1.0, 1.0, shape=(self.size,), dtype=np.float32)

 
 
     @property
@@ -440,7 +446,10 @@ 
Source code for highway_env.envs.common.action
if self.clip:
             action = np.clip(action, -1, 1)
         if self.speed_range:
-            self.controlled_vehicle.MIN_SPEED, self.controlled_vehicle.MAX_SPEED = self.speed_range
+            (
+                self.controlled_vehicle.MIN_SPEED,
+                self.controlled_vehicle.MAX_SPEED,
+            ) = self.speed_range
         if self.longitudinal and self.lateral:
             return {
                 "acceleration": utils.lmap(action[0], [-1, 1], self.acceleration_range),
@@ -454,7 +463,7 @@ 
Source code for highway_env.envs.common.action
elif self.lateral:
             return {
                 "acceleration": 0,
-                "steering": utils.lmap(action[0], [-1, 1], self.steering_range)
+                "steering": utils.lmap(action[0], [-1, 1], self.steering_range),
             }
 
 

@@ -469,18 +478,27 @@ 
Source code for highway_env.envs.common.action

 [docs]
 class DiscreteAction(ContinuousAction):
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 acceleration_range: Optional[Tuple[float, float]] = None,
-                 steering_range: Optional[Tuple[float, float]] = None,
-                 longitudinal: bool = True,
-                 lateral: bool = True,
-                 dynamical: bool = False,
-                 clip: bool = True,
-                 actions_per_axis: int = 3,
-                 **kwargs) -> None:
-        super().__init__(env, acceleration_range=acceleration_range, steering_range=steering_range,
-                         longitudinal=longitudinal, lateral=lateral, dynamical=dynamical, clip=clip)
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        acceleration_range: Optional[Tuple[float, float]] = None,
+        steering_range: Optional[Tuple[float, float]] = None,
+        longitudinal: bool = True,
+        lateral: bool = True,
+        dynamical: bool = False,
+        clip: bool = True,
+        actions_per_axis: int = 3,
+        **kwargs
+    ) -> None:
+        super().__init__(
+            env,
+            acceleration_range=acceleration_range,
+            steering_range=steering_range,
+            longitudinal=longitudinal,
+            lateral=lateral,
+            dynamical=dynamical,
+            clip=clip,
+        )
         self.actions_per_axis = actions_per_axis
 
 

@@ -508,35 +526,23 @@ 
Source code for highway_env.envs.common.action
    An discrete action space of meta-actions: lane changes, and cruise control set-point.
     """
 
-    ACTIONS_ALL = {
-        0: 'LANE_LEFT',
-        1: 'IDLE',
-        2: 'LANE_RIGHT',
-        3: 'FASTER',
-        4: 'SLOWER'
-    }
+    ACTIONS_ALL = {0: "LANE_LEFT", 1: "IDLE", 2: "LANE_RIGHT", 3: "FASTER", 4: "SLOWER"}
     """A mapping of action indexes to labels."""
 
-    ACTIONS_LONGI = {
-        0: 'SLOWER',
-        1: 'IDLE',
-        2: 'FASTER'
-    }
+    ACTIONS_LONGI = {0: "SLOWER", 1: "IDLE", 2: "FASTER"}
     """A mapping of longitudinal action indexes to labels."""
 
-    ACTIONS_LAT = {
-        0: 'LANE_LEFT',
-        1: 'IDLE',
-        2: 'LANE_RIGHT'
-    }
+    ACTIONS_LAT = {0: "LANE_LEFT", 1: "IDLE", 2: "LANE_RIGHT"}
     """A mapping of lateral action indexes to labels."""
 
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 longitudinal: bool = True,
-                 lateral: bool = True,
-                 target_speeds: Optional[Vector] = None,
-                 **kwargs) -> None:
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        longitudinal: bool = True,
+        lateral: bool = True,
+        target_speeds: Optional[Vector] = None,
+        **kwargs
+    ) -> None:
         """
         Create a discrete action space of meta-actions.
 
@@ -548,13 +554,24 @@ 
Source code for highway_env.envs.common.action
super().__init__(env)
         self.longitudinal = longitudinal
         self.lateral = lateral
-        self.target_speeds = np.array(target_speeds) if target_speeds is not None else MDPVehicle.DEFAULT_TARGET_SPEEDS
-        self.actions = self.ACTIONS_ALL if longitudinal and lateral \
-            else self.ACTIONS_LONGI if longitudinal \
-            else self.ACTIONS_LAT if lateral \
+        self.target_speeds = (
+            np.array(target_speeds)
+            if target_speeds is not None
+            else MDPVehicle.DEFAULT_TARGET_SPEEDS
+        )
+        self.actions = (
+            self.ACTIONS_ALL
+            if longitudinal and lateral
+            else self.ACTIONS_LONGI
+            if longitudinal
+            else self.ACTIONS_LAT
+            if lateral
             else None
+        )
         if self.actions is None:
-            raise ValueError("At least longitudinal or lateral actions must be included")
+            raise ValueError(
+                "At least longitudinal or lateral actions must be included"
+            )
         self.actions_indexes = {v: k for k, v in self.actions.items()}
 
 

@@ -584,21 +601,33 @@ 
Source code for highway_env.envs.common.action
        :return: the list of available actions
         """
-        actions = [self.actions_indexes['IDLE']]
+        actions = [self.actions_indexes["IDLE"]]
         network = self.controlled_vehicle.road.network
         for l_index in network.side_lanes(self.controlled_vehicle.lane_index):
-            if l_index[2] < self.controlled_vehicle.lane_index[2] \
-                    and network.get_lane(l_index).is_reachable_from(self.controlled_vehicle.position) \
-                    and self.lateral:
-                actions.append(self.actions_indexes['LANE_LEFT'])
-            if l_index[2] > self.controlled_vehicle.lane_index[2] \
-                    and network.get_lane(l_index).is_reachable_from(self.controlled_vehicle.position) \
-                    and self.lateral:
-                actions.append(self.actions_indexes['LANE_RIGHT'])
-        if self.controlled_vehicle.speed_index < self.controlled_vehicle.target_speeds.size - 1 and self.longitudinal:
-            actions.append(self.actions_indexes['FASTER'])
+            if (
+                l_index[2] < self.controlled_vehicle.lane_index[2]
+                and network.get_lane(l_index).is_reachable_from(
+                    self.controlled_vehicle.position
+                )
+                and self.lateral
+            ):
+                actions.append(self.actions_indexes["LANE_LEFT"])
+            if (
+                l_index[2] > self.controlled_vehicle.lane_index[2]
+                and network.get_lane(l_index).is_reachable_from(
+                    self.controlled_vehicle.position
+                )
+                and self.lateral
+            ):
+                actions.append(self.actions_indexes["LANE_RIGHT"])
+        if (
+            self.controlled_vehicle.speed_index
+            < self.controlled_vehicle.target_speeds.size - 1
+            and self.longitudinal
+        ):
+            actions.append(self.actions_indexes["FASTER"])
         if self.controlled_vehicle.speed_index > 0 and self.longitudinal:
-            actions.append(self.actions_indexes['SLOWER'])
+            actions.append(self.actions_indexes["SLOWER"])
         return actions

 

 
@@ -607,10 +636,7 @@ 
Source code for highway_env.envs.common.action

 [docs]
 class MultiAgentAction(ActionType):
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 action_config: dict,
-                 **kwargs) -> None:
+    def __init__(self, env: "AbstractEnv", action_config: dict, **kwargs) -> None:
         super().__init__(env)
         self.action_config = action_config
         self.agents_action_types = []
@@ -622,7 +648,9 @@ 
Source code for highway_env.envs.common.action

 [docs]
     def space(self) -> spaces.Space:
-        return spaces.Tuple([action_type.space() for action_type in self.agents_action_types])

+        return spaces.Tuple(
+            [action_type.space() for action_type in self.agents_action_types]
+        )

 
 
     @property
@@ -640,12 +668,17 @@ 
Source code for highway_env.envs.common.action

 [docs]
     def get_available_actions(self):
-        return itertools.product(*[action_type.get_available_actions() for action_type in self.agents_action_types])

+        return itertools.product(
+            *[
+                action_type.get_available_actions()
+                for action_type in self.agents_action_types
+            ]
+        )

 

 
 
 
-def action_factory(env: 'AbstractEnv', config: dict) -> ActionType:
+def action_factory(env: "AbstractEnv", config: dict) -> ActionType:
     if config["type"] == "ContinuousAction":
         return ContinuousAction(env, **config)
     if config["type"] == "DiscreteAction":
diff --git a/main/_modules/highway_env/envs/common/graphics/index.html b/main/_modules/highway_env/envs/common/graphics/index.html
index f7d2fb32f..c81109017 100644
--- a/main/_modules/highway_env/envs/common/graphics/index.html
+++ b/main/_modules/highway_env/envs/common/graphics/index.html
@@ -293,11 +293,16 @@
             
Source code for highway_env.envs.common.graphics
 import os
 from typing import TYPE_CHECKING, Callable, List, Optional
+
 import numpy as np
 import pygame
 
-from highway_env.envs.common.action import ActionType, DiscreteMetaAction, ContinuousAction
-from highway_env.road.graphics import WorldSurface, RoadGraphics
+from highway_env.envs.common.action import (
+    ActionType,
+    ContinuousAction,
+    DiscreteMetaAction,
+)
+from highway_env.road.graphics import RoadGraphics, WorldSurface
 from highway_env.vehicle.graphics import VehicleGraphics
 
 if TYPE_CHECKING:
@@ -314,7 +319,7 @@ 
Source code for highway_env.envs.common.graphics

     SAVE_IMAGES = False
     agent_display = None
 
-    def __init__(self, env: 'AbstractEnv', config: Optional[dict] = None) -> None:
+    def __init__(self, env: "AbstractEnv", config: Optional[dict] = None) -> None:
         self.env = env
         self.config = config or env.config
         self.offscreen = self.config["offscreen_rendering"]
@@ -332,12 +337,18 @@ 
Source code for highway_env.envs.common.graphics

         # instruction allows the drawing to be done on surfaces without
         # handling a screen display, useful for e.g. cloud computing
         if not self.offscreen:
-            self.screen = pygame.display.set_mode([self.config["screen_width"], self.config["screen_height"]])
+            self.screen = pygame.display.set_mode(
+                [self.config["screen_width"], self.config["screen_height"]]
+            )
         if self.agent_display:
             self.extend_display()
         self.sim_surface = WorldSurface(panel_size, 0, pygame.Surface(panel_size))
-        self.sim_surface.scaling = self.config.get("scaling", self.sim_surface.INITIAL_SCALING)
-        self.sim_surface.centering_position = self.config.get("centering_position", self.sim_surface.INITIAL_CENTERING)
+        self.sim_surface.scaling = self.config.get(
+            "scaling", self.sim_surface.INITIAL_SCALING
+        )
+        self.sim_surface.centering_position = self.config.get(
+            "centering_position", self.sim_surface.INITIAL_CENTERING
+        )
         self.clock = pygame.time.Clock()
 
         self.enabled = True
@@ -362,16 +373,20 @@ 
Source code for highway_env.envs.common.graphics

     def extend_display(self) -> None:
         if not self.offscreen:
             if self.config["screen_width"] > self.config["screen_height"]:
-                self.screen = pygame.display.set_mode((self.config["screen_width"],
-                                                       2 * self.config["screen_height"]))
+                self.screen = pygame.display.set_mode(
+                    (self.config["screen_width"], 2 * self.config["screen_height"])
+                )
             else:
-                self.screen = pygame.display.set_mode((2 * self.config["screen_width"],
-                                                       self.config["screen_height"]))
-        self.agent_surface = pygame.Surface((self.config["screen_width"], self.config["screen_height"]))
+                self.screen = pygame.display.set_mode(
+                    (2 * self.config["screen_width"], self.config["screen_height"])
+                )
+        self.agent_surface = pygame.Surface(
+            (self.config["screen_width"], self.config["screen_height"])
+        )
 
 

 [docs]
-    def set_agent_action_sequence(self, actions: List['Action']) -> None:
+    def set_agent_action_sequence(self, actions: List["Action"]) -> None:
         """
         Set the sequence of actions chosen by the agent, so that it can be displayed
 
@@ -382,10 +397,12 @@ 
Source code for highway_env.envs.common.graphics

         elif isinstance(self.env.action_type, ContinuousAction):
             actions = [self.env.action_type.get_action(a) for a in actions]
         if len(actions) > 1:
-            self.vehicle_trajectory = self.env.vehicle.predict_trajectory(actions,
-                                                                          1 / self.env.config["policy_frequency"],
-                                                                          1 / 3 / self.env.config["policy_frequency"],
-                                                                          1 / self.env.config["simulation_frequency"])

+            self.vehicle_trajectory = self.env.vehicle.predict_trajectory(
+                actions,
+                1 / self.env.config["policy_frequency"],
+                1 / 3 / self.env.config["policy_frequency"],
+                1 / self.env.config["simulation_frequency"],
+            )

 
 
 

@@ -412,29 +429,31 @@ 
Source code for highway_env.envs.common.graphics

 
         if self.vehicle_trajectory:
             VehicleGraphics.display_trajectory(
-                self.vehicle_trajectory,
-                self.sim_surface,
-                offscreen=self.offscreen)
+                self.vehicle_trajectory, self.sim_surface, offscreen=self.offscreen
+            )
 
         RoadGraphics.display_road_objects(
-            self.env.road,
-            self.sim_surface,
-            offscreen=self.offscreen
+            self.env.road, self.sim_surface, offscreen=self.offscreen
         )
 
         if EnvViewer.agent_display:
             EnvViewer.agent_display(self.agent_surface, self.sim_surface)
             if not self.offscreen:
                 if self.config["screen_width"] > self.config["screen_height"]:
-                    self.screen.blit(self.agent_surface, (0, self.config["screen_height"]))
+                    self.screen.blit(
+                        self.agent_surface, (0, self.config["screen_height"])
+                    )
                 else:
-                    self.screen.blit(self.agent_surface, (self.config["screen_width"], 0))
+                    self.screen.blit(
+                        self.agent_surface, (self.config["screen_width"], 0)
+                    )
 
         RoadGraphics.display_traffic(
             self.env.road,
             self.sim_surface,
             simulation_frequency=self.env.config["simulation_frequency"],
-            offscreen=self.offscreen)
+            offscreen=self.offscreen,
+        )
 
         ObservationGraphics.display(self.env.observation_type, self.sim_surface)
 
@@ -445,7 +464,10 @@ 
Source code for highway_env.envs.common.graphics

             pygame.display.flip()
 
         if self.SAVE_IMAGES and self.directory:
-            pygame.image.save(self.sim_surface, str(self.directory / "highway-env_{}.png".format(self.frame)))
+            pygame.image.save(
+                self.sim_surface,
+                str(self.directory / "highway-env_{}.png".format(self.frame)),
+            )
             self.frame += 1

 
 
@@ -457,7 +479,11 @@ 
Source code for highway_env.envs.common.graphics

 
         Gymnasium's channel convention is H x W x C
         """
-        surface = self.screen if self.config["render_agent"] and not self.offscreen else self.sim_surface
+        surface = (
+            self.screen
+            if self.config["render_agent"] and not self.offscreen
+            else self.sim_surface
+        )
         data = pygame.surfarray.array3d(surface)  # in W x H x C channel convention
         return np.moveaxis(data, 0, 1)

 
@@ -485,7 +511,9 @@ 
Source code for highway_env.envs.common.graphics

 
 class EventHandler(object):
     @classmethod
-    def handle_event(cls, action_type: ActionType, event: pygame.event.EventType) -> None:
+    def handle_event(
+        cls, action_type: ActionType, event: pygame.event.EventType
+    ) -> None:
         """
         Map the pygame keyboard events to control decisions
 
@@ -498,7 +526,9 @@ 
Source code for highway_env.envs.common.graphics

             cls.handle_continuous_action_event(action_type, event)
 
     @classmethod
-    def handle_discrete_action_event(cls, action_type: DiscreteMetaAction, event: pygame.event.EventType) -> None:
+    def handle_discrete_action_event(
+        cls, action_type: DiscreteMetaAction, event: pygame.event.EventType
+    ) -> None:
         if event.type == pygame.KEYDOWN:
             if event.key == pygame.K_RIGHT and action_type.longitudinal:
                 action_type.act(action_type.actions_indexes["FASTER"])
@@ -510,7 +540,9 @@ 
Source code for highway_env.envs.common.graphics

                 action_type.act(action_type.actions_indexes["LANE_LEFT"])
 
     @classmethod
-    def handle_continuous_action_event(cls, action_type: ContinuousAction, event: pygame.event.EventType) -> None:
+    def handle_continuous_action_event(
+        cls, action_type: ContinuousAction, event: pygame.event.EventType
+    ) -> None:
         action = action_type.last_action.copy()
         steering_index = action_type.space().shape[0] - 1
         if event.type == pygame.KEYDOWN:
@@ -540,19 +572,33 @@ 
Source code for highway_env.envs.common.graphics

     @classmethod
     def display(cls, obs, sim_surface):
         from highway_env.envs.common.observation import LidarObservation
+
         if isinstance(obs, LidarObservation):
             cls.display_grid(obs, sim_surface)
 
     @classmethod
     def display_grid(cls, lidar_observation, surface):
-        psi = np.repeat(np.arange(-lidar_observation.angle/2,
-                                  2 * np.pi - lidar_observation.angle/2,
-                                  2 * np.pi / lidar_observation.grid.shape[0]), 2)
+        psi = np.repeat(
+            np.arange(
+                -lidar_observation.angle / 2,
+                2 * np.pi - lidar_observation.angle / 2,
+                2 * np.pi / lidar_observation.grid.shape[0],
+            ),
+            2,
+        )
         psi = np.hstack((psi[1:], [psi[0]]))
-        r = np.repeat(np.minimum(lidar_observation.grid[:, 0], lidar_observation.maximum_range), 2)
-        points = [(surface.pos2pix(lidar_observation.origin[0] + r[i] * np.cos(psi[i]),
-                                   lidar_observation.origin[1] + r[i] * np.sin(psi[i])))
-                  for i in range(np.size(psi))]
+        r = np.repeat(
+            np.minimum(lidar_observation.grid[:, 0], lidar_observation.maximum_range), 2
+        )
+        points = [
+            (
+                surface.pos2pix(
+                    lidar_observation.origin[0] + r[i] * np.cos(psi[i]),
+                    lidar_observation.origin[1] + r[i] * np.sin(psi[i]),
+                )
+            )
+            for i in range(np.size(psi))
+        ]
         pygame.draw.lines(surface, ObservationGraphics.COLOR, True, points, 1)
 

           
diff --git a/main/_modules/highway_env/envs/common/observation/index.html b/main/_modules/highway_env/envs/common/observation/index.html
index 6df9d763f..33288dc47 100644
--- a/main/_modules/highway_env/envs/common/observation/index.html
+++ b/main/_modules/highway_env/envs/common/observation/index.html
@@ -293,17 +293,17 @@
             
Source code for highway_env.envs.common.observation
 from collections import OrderedDict
 from itertools import product
-from typing import List, Dict, TYPE_CHECKING, Optional, Union, Tuple
-from gymnasium import spaces
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+
 import numpy as np
 import pandas as pd
+from gymnasium import spaces
 
 from highway_env import utils
 from highway_env.envs.common.finite_mdp import compute_ttc_grid
 from highway_env.envs.common.graphics import EnvViewer
 from highway_env.road.lane import AbstractLane
-from highway_env.utils import distance_to_circle, Vector
-from highway_env.vehicle.controller import MDPVehicle
+from highway_env.utils import Vector
 from highway_env.vehicle.kinematics import Vehicle
 
 if TYPE_CHECKING:
@@ -311,7 +311,7 @@ 
Source code for highway_env.envs.common.observation
class ObservationType(object):
-    def __init__(self, env: 'AbstractEnv', **kwargs) -> None:
+    def __init__(self, env: "AbstractEnv", **kwargs) -> None:
         self.env = env
         self.__observer_vehicle = None
 
@@ -357,28 +357,34 @@ 
Source code for highway_env.envs.common.observation
        }
     """
 
-    def __init__(self, env: 'AbstractEnv',
-                 observation_shape: Tuple[int, int],
-                 stack_size: int,
-                 weights: List[float],
-                 scaling: Optional[float] = None,
-                 centering_position: Optional[List[float]] = None,
-                 **kwargs) -> None:
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        observation_shape: Tuple[int, int],
+        stack_size: int,
+        weights: List[float],
+        scaling: Optional[float] = None,
+        centering_position: Optional[List[float]] = None,
+        **kwargs
+    ) -> None:
         super().__init__(env)
         self.observation_shape = observation_shape
-        self.shape = (stack_size, ) + self.observation_shape
+        self.shape = (stack_size,) + self.observation_shape
         self.weights = weights
         self.obs = np.zeros(self.shape, dtype=np.uint8)
 
         # The viewer configuration can be different between this observation and env.render() (typically smaller)
         viewer_config = env.config.copy()
-        viewer_config.update({
-            "offscreen_rendering": True,
-            "screen_width": self.observation_shape[0],
-            "screen_height": self.observation_shape[1],
-            "scaling": scaling or viewer_config["scaling"],
-            "centering_position": centering_position or viewer_config["centering_position"]
-        })
+        viewer_config.update(
+            {
+                "offscreen_rendering": True,
+                "screen_width": self.observation_shape[0],
+                "screen_height": self.observation_shape[1],
+                "scaling": scaling or viewer_config["scaling"],
+                "centering_position": centering_position
+                or viewer_config["centering_position"],
+            }
+        )
         self.viewer = EnvViewer(env, config=viewer_config)
 
 

@@ -406,34 +412,42 @@ 
Source code for highway_env.envs.common.observation
class TimeToCollisionObservation(ObservationType):
-    def __init__(self, env: 'AbstractEnv', horizon: int = 10, **kwargs: dict) -> None:
+    def __init__(self, env: "AbstractEnv", horizon: int = 10, **kwargs: dict) -> None:
         super().__init__(env)
         self.horizon = horizon
 
     def space(self) -> spaces.Space:
         try:
-            return spaces.Box(shape=self.observe().shape, low=0, high=1, dtype=np.float32)
+            return spaces.Box(
+                shape=self.observe().shape, low=0, high=1, dtype=np.float32
+            )
         except AttributeError:
             return spaces.Space()
 
     def observe(self) -> np.ndarray:
         if not self.env.road:
-            return np.zeros((3, 3, int(self.horizon * self.env.config["policy_frequency"])))
-        grid = compute_ttc_grid(self.env, vehicle=self.observer_vehicle,
-                                time_quantization=1/self.env.config["policy_frequency"], horizon=self.horizon)
+            return np.zeros(
+                (3, 3, int(self.horizon * self.env.config["policy_frequency"]))
+            )
+        grid = compute_ttc_grid(
+            self.env,
+            vehicle=self.observer_vehicle,
+            time_quantization=1 / self.env.config["policy_frequency"],
+            horizon=self.horizon,
+        )
         padding = np.ones(np.shape(grid))
         padded_grid = np.concatenate([padding, grid, padding], axis=1)
         obs_lanes = 3
         l0 = grid.shape[1] + self.observer_vehicle.lane_index[2] - obs_lanes // 2
         lf = grid.shape[1] + self.observer_vehicle.lane_index[2] + obs_lanes // 2
-        clamped_grid = padded_grid[:, l0:lf+1, :]
+        clamped_grid = padded_grid[:, l0 : lf + 1, :]
         repeats = np.ones(clamped_grid.shape[0])
         repeats[np.array([0, -1])] += clamped_grid.shape[0]
         padded_grid = np.repeat(clamped_grid, repeats.astype(int), axis=0)
         obs_speeds = 3
         v0 = grid.shape[0] + self.observer_vehicle.speed_index - obs_speeds // 2
         vf = grid.shape[0] + self.observer_vehicle.speed_index + obs_speeds // 2
-        clamped_grid = padded_grid[v0:vf + 1, :, :]
+        clamped_grid = padded_grid[v0 : vf + 1, :, :]
         return clamped_grid.astype(np.float32)
 
 
@@ -443,20 +457,23 @@ 
Source code for highway_env.envs.common.observation
    """Observe the kinematics of nearby vehicles."""
 
-    FEATURES: List[str] = ['presence', 'x', 'y', 'vx', 'vy']
-
-    def __init__(self, env: 'AbstractEnv',
-                 features: List[str] = None,
-                 vehicles_count: int = 5,
-                 features_range: Dict[str, List[float]] = None,
-                 absolute: bool = False,
-                 order: str = "sorted",
-                 normalize: bool = True,
-                 clip: bool = True,
-                 see_behind: bool = False,
-                 observe_intentions: bool = False,
-                 include_obstacles: bool = True,
-                 **kwargs: dict) -> None:
+    FEATURES: List[str] = ["presence", "x", "y", "vx", "vy"]
+
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        features: List[str] = None,
+        vehicles_count: int = 5,
+        features_range: Dict[str, List[float]] = None,
+        absolute: bool = False,
+        order: str = "sorted",
+        normalize: bool = True,
+        clip: bool = True,
+        see_behind: bool = False,
+        observe_intentions: bool = False,
+        include_obstacles: bool = True,
+        **kwargs: dict
+    ) -> None:
         """
         :param env: The environment to observe
         :param features: Names of features used in the observation
@@ -484,7 +501,12 @@ 
Source code for highway_env.envs.common.observation

 [docs]
     def space(self) -> spaces.Space:
-        return spaces.Box(shape=(self.vehicles_count, len(self.features)), low=-np.inf, high=np.inf, dtype=np.float32)

+        return spaces.Box(
+            shape=(self.vehicles_count, len(self.features)),
+            low=-np.inf,
+            high=np.inf,
+            dtype=np.float32,
+        )

 
 
 

@@ -497,12 +519,17 @@ 
Source code for highway_env.envs.common.observation
        :param Dataframe df: observation data
         """
         if not self.features_range:
-            side_lanes = self.env.road.network.all_side_lanes(self.observer_vehicle.lane_index)
+            side_lanes = self.env.road.network.all_side_lanes(
+                self.observer_vehicle.lane_index
+            )
             self.features_range = {
                 "x": [-5.0 * Vehicle.MAX_SPEED, 5.0 * Vehicle.MAX_SPEED],
-                "y": [-AbstractLane.DEFAULT_WIDTH * len(side_lanes), AbstractLane.DEFAULT_WIDTH * len(side_lanes)],
-                "vx": [-2*Vehicle.MAX_SPEED, 2*Vehicle.MAX_SPEED],
-                "vy": [-2*Vehicle.MAX_SPEED, 2*Vehicle.MAX_SPEED]
+                "y": [
+                    -AbstractLane.DEFAULT_WIDTH * len(side_lanes),
+                    AbstractLane.DEFAULT_WIDTH * len(side_lanes),
+                ],
+                "vx": [-2 * Vehicle.MAX_SPEED, 2 * Vehicle.MAX_SPEED],
+                "vy": [-2 * Vehicle.MAX_SPEED, 2 * Vehicle.MAX_SPEED],
             }
         for feature, f_range in self.features_range.items():
             if feature in df:
@@ -521,17 +548,22 @@ 
Source code for highway_env.envs.common.observation
# Add ego-vehicle
         df = pd.DataFrame.from_records([self.observer_vehicle.to_dict()])
         # Add nearby traffic
-        close_vehicles = self.env.road.close_objects_to(self.observer_vehicle,
-                                                        self.env.PERCEPTION_DISTANCE,
-                                                        count=self.vehicles_count - 1,
-                                                        see_behind=self.see_behind,
-                                                        sort=self.order == "sorted",
-                                                        vehicles_only=not self.include_obstacles)
+        close_vehicles = self.env.road.close_objects_to(
+            self.observer_vehicle,
+            self.env.PERCEPTION_DISTANCE,
+            count=self.vehicles_count - 1,
+            see_behind=self.see_behind,
+            sort=self.order == "sorted",
+            vehicles_only=not self.include_obstacles,
+        )
         if close_vehicles:
             origin = self.observer_vehicle if not self.absolute else None
             vehicles_df = pd.DataFrame.from_records(
-                [v.to_dict(origin, observe_intentions=self.observe_intentions)
-                 for v in close_vehicles[-self.vehicles_count + 1:]])
+                [
+                    v.to_dict(origin, observe_intentions=self.observe_intentions)
+                    for v in close_vehicles[-self.vehicles_count + 1 :]
+                ]
+            )
             df = pd.concat([df, vehicles_df], ignore_index=True)
 
         df = df[self.features]
@@ -542,7 +574,9 @@ 
Source code for highway_env.envs.common.observation
# Fill missing rows
         if df.shape[0] < self.vehicles_count:
             rows = np.zeros((self.vehicles_count - df.shape[0], len(self.features)))
-            df = pd.concat([df, pd.DataFrame(data=rows, columns=self.features)], ignore_index=True)
+            df = pd.concat(
+                [df, pd.DataFrame(data=rows, columns=self.features)], ignore_index=True
+            )
         # Reorder
         df = df[self.features]
         obs = df.values.copy()
@@ -560,21 +594,23 @@ 
Source code for highway_env.envs.common.observation
    """Observe an occupancy grid of nearby vehicles."""
 
-    FEATURES: List[str] = ['presence', 'vx', 'vy', 'on_road']
-    GRID_SIZE: List[List[float]] = [[-5.5*5, 5.5*5], [-5.5*5, 5.5*5]]
+    FEATURES: List[str] = ["presence", "vx", "vy", "on_road"]
+    GRID_SIZE: List[List[float]] = [[-5.5 * 5, 5.5 * 5], [-5.5 * 5, 5.5 * 5]]
     GRID_STEP: List[int] = [5, 5]
 
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 features: Optional[List[str]] = None,
-                 grid_size: Optional[Tuple[Tuple[float, float], Tuple[float, float]]] = None,
-                 grid_step: Optional[Tuple[float, float]] = None,
-                 features_range: Dict[str, List[float]] = None,
-                 absolute: bool = False,
-                 align_to_vehicle_axes: bool = False,
-                 clip: bool = True,
-                 as_image: bool = False,
-                 **kwargs: dict) -> None:
+    def __init__(
+        self,
+        env: "AbstractEnv",
+        features: Optional[List[str]] = None,
+        grid_size: Optional[Tuple[Tuple[float, float], Tuple[float, float]]] = None,
+        grid_step: Optional[Tuple[float, float]] = None,
+        features_range: Dict[str, List[float]] = None,
+        absolute: bool = False,
+        align_to_vehicle_axes: bool = False,
+        clip: bool = True,
+        as_image: bool = False,
+        **kwargs: dict
+    ) -> None:
         """
         :param env: The environment to observe
         :param features: Names of features used in the observation
@@ -588,10 +624,16 @@ 
Source code for highway_env.envs.common.observation
        """
         super().__init__(env)
         self.features = features if features is not None else self.FEATURES
-        self.grid_size = np.array(grid_size) if grid_size is not None else np.array(self.GRID_SIZE)
-        self.grid_step = np.array(grid_step) if grid_step is not None else np.array(self.GRID_STEP)
-        grid_shape = np.asarray(np.floor((self.grid_size[:, 1] - self.grid_size[:, 0]) / self.grid_step),
-                                dtype=np.uint8)
+        self.grid_size = (
+            np.array(grid_size) if grid_size is not None else np.array(self.GRID_SIZE)
+        )
+        self.grid_step = (
+            np.array(grid_step) if grid_step is not None else np.array(self.GRID_STEP)
+        )
+        grid_shape = np.asarray(
+            np.floor((self.grid_size[:, 1] - self.grid_size[:, 0]) / self.grid_step),
+            dtype=np.uint8,
+        )
         self.grid = np.zeros((len(self.features), *grid_shape))
         self.features_range = features_range
         self.absolute = absolute
@@ -605,7 +647,9 @@ 
Source code for highway_env.envs.common.observation
if self.as_image:
             return spaces.Box(shape=self.grid.shape, low=0, high=255, dtype=np.uint8)
         else:
-            return spaces.Box(shape=self.grid.shape, low=-np.inf, high=np.inf, dtype=np.float32)

+            return spaces.Box(
+                shape=self.grid.shape, low=-np.inf, high=np.inf, dtype=np.float32
+            )

 
 
 

@@ -619,8 +663,8 @@ 
Source code for highway_env.envs.common.observation
        """
         if not self.features_range:
             self.features_range = {
-                "vx": [-2*Vehicle.MAX_SPEED, 2*Vehicle.MAX_SPEED],
-                "vy": [-2*Vehicle.MAX_SPEED, 2*Vehicle.MAX_SPEED]
+                "vx": [-2 * Vehicle.MAX_SPEED, 2 * Vehicle.MAX_SPEED],
+                "vy": [-2 * Vehicle.MAX_SPEED, 2 * Vehicle.MAX_SPEED],
             }
         for feature, f_range in self.features_range.items():
             if feature in df:
@@ -642,7 +686,8 @@ 
Source code for highway_env.envs.common.observation
# Get nearby traffic data
             df = pd.DataFrame.from_records(
-                [v.to_dict(self.observer_vehicle) for v in self.env.road.vehicles])
+                [v.to_dict(self.observer_vehicle) for v in self.env.road.vehicles]
+            )
             # Normalize
             df = self.normalize(df)
             # Fill-in features
@@ -652,11 +697,28 @@ 
Source code for highway_env.envs.common.observation
x, y = vehicle["x"], vehicle["y"]
                         # Recover unnormalized coordinates for cell index
                         if "x" in self.features_range:
-                            x = utils.lmap(x, [-1, 1], [self.features_range["x"][0], self.features_range["x"][1]])
+                            x = utils.lmap(
+                                x,
+                                [-1, 1],
+                                [
+                                    self.features_range["x"][0],
+                                    self.features_range["x"][1],
+                                ],
+                            )
                         if "y" in self.features_range:
-                            y = utils.lmap(y, [-1, 1], [self.features_range["y"][0], self.features_range["y"][1]])
+                            y = utils.lmap(
+                                y,
+                                [-1, 1],
+                                [
+                                    self.features_range["y"][0],
+                                    self.features_range["y"][1],
+                                ],
+                            )
                         cell = self.pos_to_index((x, y), relative=not self.absolute)
-                        if 0 <= cell[1] < self.grid.shape[-2] and 0 <= cell[0] < self.grid.shape[-1]:
+                        if (
+                            0 <= cell[1] < self.grid.shape[-2]
+                            and 0 <= cell[0] < self.grid.shape[-1]
+                        ):
                             self.grid[layer, cell[1], cell[0]] = vehicle[feature]
                 elif feature == "on_road":
                     self.fill_road_layer_by_lanes(layer)
@@ -689,20 +751,26 @@ 
Source code for highway_env.envs.common.observation
if not relative:
             position -= self.observer_vehicle.position
         if self.align_to_vehicle_axes:
-            c, s = np.cos(self.observer_vehicle.heading), np.sin(self.observer_vehicle.heading)
+            c, s = np.cos(self.observer_vehicle.heading), np.sin(
+                self.observer_vehicle.heading
+            )
             position = np.array([[c, s], [-s, c]]) @ position
-        return int(np.floor((position[0] - self.grid_size[0, 0]) / self.grid_step[0])),\
-               int(np.floor((position[1] - self.grid_size[1, 0]) / self.grid_step[1]))

+        return int(
+            np.floor((position[0] - self.grid_size[0, 0]) / self.grid_step[0])
+        ), int(np.floor((position[1] - self.grid_size[1, 0]) / self.grid_step[1]))

 
 
     def index_to_pos(self, index: Tuple[int, int]) -> np.ndarray:
-
-        position = np.array([
-            (index[1] + 0.5) * self.grid_step[0] + self.grid_size[0, 0],
-            (index[0] + 0.5) * self.grid_step[1] + self.grid_size[1, 0]
-        ])
+        position = np.array(
+            [
+                (index[1] + 0.5) * self.grid_step[0] + self.grid_size[0, 0],
+                (index[0] + 0.5) * self.grid_step[1] + self.grid_size[1, 0],
+            ]
+        )
         if self.align_to_vehicle_axes:
-            c, s = np.cos(-self.observer_vehicle.heading), np.sin(-self.observer_vehicle.heading)
+            c, s = np.cos(-self.observer_vehicle.heading), np.sin(
+                -self.observer_vehicle.heading
+            )
             position = np.array([[c, s], [-s, c]]) @ position
 
         position += self.observer_vehicle.position
@@ -710,7 +778,9 @@ 
Source code for highway_env.envs.common.observation

 [docs]
-    def fill_road_layer_by_lanes(self, layer_index: int, lane_perception_distance: float = 100) -> None:
+    def fill_road_layer_by_lanes(
+        self, layer_index: int, lane_perception_distance: float = 100
+    ) -> None:
         """
         A layer to encode the onroad (1) / offroad (0) information
 
@@ -727,12 +797,17 @@ 
Source code for highway_env.envs.common.observation
for _to in road.network.graph[_from].keys():
                 for lane in road.network.graph[_from][_to]:
                     origin, _ = lane.local_coordinates(self.observer_vehicle.position)
-                    waypoints = np.arange(origin - lane_perception_distance,
-                                            origin + lane_perception_distance,
-                                            lane_waypoints_spacing).clip(0, lane.length)
+                    waypoints = np.arange(
+                        origin - lane_perception_distance,
+                        origin + lane_perception_distance,
+                        lane_waypoints_spacing,
+                    ).clip(0, lane.length)
                     for waypoint in waypoints:
                         cell = self.pos_to_index(lane.position(waypoint, 0))
-                        if 0 <= cell[1] < self.grid.shape[-2] and 0 <= cell[0] < self.grid.shape[-1]:
+                        if (
+                            0 <= cell[1] < self.grid.shape[-2]
+                            and 0 <= cell[0] < self.grid.shape[-1]
+                        ):
                             self.grid[layer_index, cell[1], cell[0]] = 1

 
 
@@ -759,7 +834,7 @@ 
Source code for highway_env.envs.common.observation

 [docs]
 class KinematicsGoalObservation(KinematicObservation):
-    def __init__(self, env: 'AbstractEnv', scales: List[float], **kwargs: dict) -> None:
+    def __init__(self, env: "AbstractEnv", scales: List[float], **kwargs: dict) -> None:
         self.scales = np.array(scales)
         super().__init__(env, **kwargs)
 
@@ -768,11 +843,28 @@ 
Source code for highway_env.envs.common.observation
def space(self) -> spaces.Space:
         try:
             obs = self.observe()
-            return spaces.Dict(dict(
-                desired_goal=spaces.Box(-np.inf, np.inf, shape=obs["desired_goal"].shape, dtype=np.float64),
-                achieved_goal=spaces.Box(-np.inf, np.inf, shape=obs["achieved_goal"].shape, dtype=np.float64),
-                observation=spaces.Box(-np.inf, np.inf, shape=obs["observation"].shape, dtype=np.float64),
-            ))
+            return spaces.Dict(
+                dict(
+                    desired_goal=spaces.Box(
+                        -np.inf,
+                        np.inf,
+                        shape=obs["desired_goal"].shape,
+                        dtype=np.float64,
+                    ),
+                    achieved_goal=spaces.Box(
+                        -np.inf,
+                        np.inf,
+                        shape=obs["achieved_goal"].shape,
+                        dtype=np.float64,
+                    ),
+                    observation=spaces.Box(
+                        -np.inf,
+                        np.inf,
+                        shape=obs["observation"].shape,
+                        dtype=np.float64,
+                    ),
+                )
+            )
         except AttributeError:
             return spaces.Space()

 
@@ -781,50 +873,61 @@ 
Source code for highway_env.envs.common.observation
[docs]
     def observe(self) -> Dict[str, np.ndarray]:
         if not self.observer_vehicle:
-            return OrderedDict([
-                ("observation", np.zeros((len(self.features),))),
-                ("achieved_goal", np.zeros((len(self.features),))),
-                ("desired_goal", np.zeros((len(self.features),)))
-            ])
-
-        obs = np.ravel(pd.DataFrame.from_records([self.observer_vehicle.to_dict()])[self.features])
-        goal = np.ravel(pd.DataFrame.from_records([self.env.goal.to_dict()])[self.features])
-        obs = OrderedDict([
-            ("observation", obs / self.scales),
-            ("achieved_goal", obs / self.scales),
-            ("desired_goal", goal / self.scales)
-         ])
+            return OrderedDict(
+                [
+                    ("observation", np.zeros((len(self.features),))),
+                    ("achieved_goal", np.zeros((len(self.features),))),
+                    ("desired_goal", np.zeros((len(self.features),))),
+                ]
+            )
+
+        obs = np.ravel(
+            pd.DataFrame.from_records([self.observer_vehicle.to_dict()])[self.features]
+        )
+        goal = np.ravel(
+            pd.DataFrame.from_records([self.env.goal.to_dict()])[self.features]
+        )
+        obs = OrderedDict(
+            [
+                ("observation", obs / self.scales),
+                ("achieved_goal", obs / self.scales),
+                ("desired_goal", goal / self.scales),
+            ]
+        )
         return obs

 

 
 
 
 class AttributesObservation(ObservationType):
-    def __init__(self, env: 'AbstractEnv', attributes: List[str], **kwargs: dict) -> None:
+    def __init__(
+        self, env: "AbstractEnv", attributes: List[str], **kwargs: dict
+    ) -> None:
         self.env = env
         self.attributes = attributes
 
     def space(self) -> spaces.Space:
         try:
             obs = self.observe()
-            return spaces.Dict({
-                attribute: spaces.Box(-np.inf, np.inf, shape=obs[attribute].shape, dtype=np.float64)
-                for attribute in self.attributes
-            })
+            return spaces.Dict(
+                {
+                    attribute: spaces.Box(
+                        -np.inf, np.inf, shape=obs[attribute].shape, dtype=np.float64
+                    )
+                    for attribute in self.attributes
+                }
+            )
         except AttributeError:
             return spaces.Space()
 
     def observe(self) -> Dict[str, np.ndarray]:
-        return OrderedDict([
-            (attribute, getattr(self.env, attribute)) for attribute in self.attributes
-        ])
+        return OrderedDict(
+            [(attribute, getattr(self.env, attribute)) for attribute in self.attributes]
+        )
 
 
 class MultiAgentObservation(ObservationType):
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 observation_config: dict,
-                 **kwargs) -> None:
+    def __init__(self, env: "AbstractEnv", observation_config: dict, **kwargs) -> None:
         super().__init__(env)
         self.observation_config = observation_config
         self.agents_observation_types = []
@@ -834,19 +937,23 @@ 
Source code for highway_env.envs.common.observation
self.agents_observation_types.append(obs_type)
 
     def space(self) -> spaces.Space:
-        return spaces.Tuple([obs_type.space() for obs_type in self.agents_observation_types])
+        return spaces.Tuple(
+            [obs_type.space() for obs_type in self.agents_observation_types]
+        )
 
     def observe(self) -> tuple:
         return tuple(obs_type.observe() for obs_type in self.agents_observation_types)
 
 
 class TupleObservation(ObservationType):
-    def __init__(self,
-                 env: 'AbstractEnv',
-                 observation_configs: List[dict],
-                 **kwargs) -> None:
+    def __init__(
+        self, env: "AbstractEnv", observation_configs: List[dict], **kwargs
+    ) -> None:
         super().__init__(env)
-        self.observation_types = [observation_factory(self.env, obs_config) for obs_config in observation_configs]
+        self.observation_types = [
+            observation_factory(self.env, obs_config)
+            for obs_config in observation_configs
+        ]
 
     def space(self) -> spaces.Space:
         return spaces.Tuple([obs_type.space() for obs_type in self.observation_types])
@@ -874,23 +981,37 @@ 
Source code for highway_env.envs.common.observation
df = pd.DataFrame.from_records([ego_dict])[self.features]
 
         # Add nearby traffic
-        close_vehicles = self.env.road.close_vehicles_to(self.observer_vehicle,
-                                                         self.env.PERCEPTION_DISTANCE,
-                                                         count=self.vehicles_count - 1,
-                                                         see_behind=self.see_behind)
+        close_vehicles = self.env.road.close_vehicles_to(
+            self.observer_vehicle,
+            self.env.PERCEPTION_DISTANCE,
+            count=self.vehicles_count - 1,
+            see_behind=self.see_behind,
+        )
         if close_vehicles:
             origin = self.observer_vehicle if not self.absolute else None
-            df = pd.concat([df, pd.DataFrame.from_records(
-                [v.to_dict(origin, observe_intentions=self.observe_intentions)
-                 for v in close_vehicles[-self.vehicles_count + 1:]])[self.features]],
-                           ignore_index=True)
+            df = pd.concat(
+                [
+                    df,
+                    pd.DataFrame.from_records(
+                        [
+                            v.to_dict(
+                                origin, observe_intentions=self.observe_intentions
+                            )
+                            for v in close_vehicles[-self.vehicles_count + 1 :]
+                        ]
+                    )[self.features],
+                ],
+                ignore_index=True,
+            )
         # Normalize and clip
         if self.normalize:
             df = self.normalize_obs(df)
         # Fill missing rows
         if df.shape[0] < self.vehicles_count:
             rows = np.zeros((self.vehicles_count - df.shape[0], len(self.features)))
-            df = pd.concat([df, pd.DataFrame(data=rows, columns=self.features)], ignore_index=True)
+            df = pd.concat(
+                [df, pd.DataFrame(data=rows, columns=self.features)], ignore_index=True
+            )
         # Reorder
         df = df[self.features]
         obs = df.values.copy()
@@ -906,17 +1027,20 @@ 
Source code for highway_env.envs.common.observation
DISTANCE = 0
     SPEED = 1
 
-    def __init__(self, env,
-                 cells: int = 16,
-                 maximum_range: float = 60,
-                 normalize: bool = True,
-                 **kwargs):
+    def __init__(
+        self,
+        env,
+        cells: int = 16,
+        maximum_range: float = 60,
+        normalize: bool = True,
+        **kwargs
+    ):
         super().__init__(env, **kwargs)
         self.cells = cells
         self.maximum_range = maximum_range
         self.normalize = normalize
         self.angle = 2 * np.pi / self.cells
-        self.grid = np.ones((self.cells, 1)) * float('inf')
+        self.grid = np.ones((self.cells, 1)) * float("inf")
         self.origin = None
 
     def space(self) -> spaces.Space:
@@ -924,7 +1048,9 @@ 
Source code for highway_env.envs.common.observation
return spaces.Box(shape=(self.cells, 2), low=-high, high=high, dtype=np.float32)
 
     def observe(self) -> np.ndarray:
-        obs = self.trace(self.observer_vehicle.position, self.observer_vehicle.velocity).copy()
+        obs = self.trace(
+            self.observer_vehicle.position, self.observer_vehicle.velocity
+        ).copy()
         if self.normalize:
             obs /= self.maximum_range
         return obs
@@ -948,16 +1074,22 @@ 
Source code for highway_env.envs.common.observation
self.grid[center_index, :] = [distance, velocity]
 
             # Angular sector covered by the obstacle
-            corners = utils.rect_corners(obstacle.position, obstacle.LENGTH, obstacle.WIDTH, obstacle.heading)
+            corners = utils.rect_corners(
+                obstacle.position, obstacle.LENGTH, obstacle.WIDTH, obstacle.heading
+            )
             angles = [self.position_to_angle(corner, origin) for corner in corners]
             min_angle, max_angle = min(angles), max(angles)
-            if min_angle < -np.pi/2 < np.pi/2 < max_angle:  # Object's corners are wrapping around +pi
-                min_angle, max_angle = max_angle, min_angle + 2*np.pi
+            if (
+                min_angle < -np.pi / 2 < np.pi / 2 < max_angle
+            ):  # Object's corners are wrapping around +pi
+                min_angle, max_angle = max_angle, min_angle + 2 * np.pi
             start, end = self.angle_to_index(min_angle), self.angle_to_index(max_angle)
             if start < end:
-                indexes = np.arange(start, end+1)
+                indexes = np.arange(start, end + 1)
             else:  # Object's corners are wrapping around 0
-                indexes = np.hstack([np.arange(start, self.cells), np.arange(0, end + 1)])
+                indexes = np.hstack(
+                    [np.arange(start, self.cells), np.arange(0, end + 1)]
+                )
 
             # Actual distance computation for these sections
             for index in indexes:
@@ -970,7 +1102,10 @@ 
Source code for highway_env.envs.common.observation
return self.grid
 
     def position_to_angle(self, position: np.ndarray, origin: np.ndarray) -> float:
-        return np.arctan2(position[1] - origin[1], position[0] - origin[0]) + self.angle/2
+        return (
+            np.arctan2(position[1] - origin[1], position[0] - origin[0])
+            + self.angle / 2
+        )
 
     def position_to_index(self, position: np.ndarray, origin: np.ndarray) -> int:
         return self.angle_to_index(self.position_to_angle(position, origin))
@@ -982,7 +1117,7 @@ 
Source code for highway_env.envs.common.observation
return np.array([np.cos(index * self.angle), np.sin(index * self.angle)])
 
 
-def observation_factory(env: 'AbstractEnv', config: dict) -> ObservationType:
+def observation_factory(env: "AbstractEnv", config: dict) -> ObservationType:
     if config["type"] == "TimeToCollision":
         return TimeToCollisionObservation(env, **config)
     elif config["type"] == "Kinematics":
diff --git a/main/_modules/highway_env/envs/highway_env/index.html b/main/_modules/highway_env/envs/highway_env/index.html
index d686e9e1f..e42242034 100644
--- a/main/_modules/highway_env/envs/highway_env/index.html
+++ b/main/_modules/highway_env/envs/highway_env/index.html
@@ -321,30 +321,30 @@ 
Source code for highway_env.envs.highway_env
@classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "Kinematics"
-            },
-            "action": {
-                "type": "DiscreteMetaAction",
-            },
-            "lanes_count": 4,
-            "vehicles_count": 50,
-            "controlled_vehicles": 1,
-            "initial_lane_id": None,
-            "duration": 40,  # [s]
-            "ego_spacing": 2,
-            "vehicles_density": 1,
-            "collision_reward": -1,    # The reward received when colliding with a vehicle.
-            "right_lane_reward": 0.1,  # The reward received when driving on the right-most lanes, linearly mapped to
-                                       # zero for other lanes.
-            "high_speed_reward": 0.4,  # The reward received when driving at full speed, linearly mapped to zero for
-                                       # lower speeds according to config["reward_speed_range"].
-            "lane_change_reward": 0,   # The reward received at each lane change action.
-            "reward_speed_range": [20, 30],
-            "normalize_reward": True,
-            "offroad_terminal": False
-        })
+        config.update(
+            {
+                "observation": {"type": "Kinematics"},
+                "action": {
+                    "type": "DiscreteMetaAction",
+                },
+                "lanes_count": 4,
+                "vehicles_count": 50,
+                "controlled_vehicles": 1,
+                "initial_lane_id": None,
+                "duration": 40,  # [s]
+                "ego_spacing": 2,
+                "vehicles_density": 1,
+                "collision_reward": -1,  # The reward received when colliding with a vehicle.
+                "right_lane_reward": 0.1,  # The reward received when driving on the right-most lanes, linearly mapped to
+                # zero for other lanes.
+                "high_speed_reward": 0.4,  # The reward received when driving at full speed, linearly mapped to zero for
+                # lower speeds according to config["reward_speed_range"].
+                "lane_change_reward": 0,  # The reward received at each lane change action.
+                "reward_speed_range": [20, 30],
+                "normalize_reward": True,
+                "offroad_terminal": False,
+            }
+        )
         return config

 
 
@@ -354,13 +354,20 @@ 
Source code for highway_env.envs.highway_env
def _create_road(self) -> None:
         """Create a road composed of straight adjacent lanes."""
-        self.road = Road(network=RoadNetwork.straight_road_network(self.config["lanes_count"], speed_limit=30),
-                         np_random=self.np_random, record_history=self.config["show_trajectories"])
+        self.road = Road(
+            network=RoadNetwork.straight_road_network(
+                self.config["lanes_count"], speed_limit=30
+            ),
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
 
     def _create_vehicles(self) -> None:
         """Create some new random vehicles of a given type, and add them on the road."""
         other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
-        other_per_controlled = near_split(self.config["vehicles_count"], num_bins=self.config["controlled_vehicles"])
+        other_per_controlled = near_split(
+            self.config["vehicles_count"], num_bins=self.config["controlled_vehicles"]
+        )
 
         self.controlled_vehicles = []
         for others in other_per_controlled:
@@ -368,14 +375,18 @@ 
Source code for highway_env.envs.highway_env
self.road,
                 speed=25,
                 lane_id=self.config["initial_lane_id"],
-                spacing=self.config["ego_spacing"]
+                spacing=self.config["ego_spacing"],
+            )
+            vehicle = self.action_type.vehicle_class(
+                self.road, vehicle.position, vehicle.heading, vehicle.speed
             )
-            vehicle = self.action_type.vehicle_class(self.road, vehicle.position, vehicle.heading, vehicle.speed)
             self.controlled_vehicles.append(vehicle)
             self.road.vehicles.append(vehicle)
 
             for _ in range(others):
-                vehicle = other_vehicles_type.create_random(self.road, spacing=1 / self.config["vehicles_density"])
+                vehicle = other_vehicles_type.create_random(
+                    self.road, spacing=1 / self.config["vehicles_density"]
+                )
                 vehicle.randomize_behavior()
                 self.road.vehicles.append(vehicle)
 
@@ -386,33 +397,47 @@ 
Source code for highway_env.envs.highway_env
        :return: the corresponding reward
         """
         rewards = self._rewards(action)
-        reward = sum(self.config.get(name, 0) * reward for name, reward in rewards.items())
+        reward = sum(
+            self.config.get(name, 0) * reward for name, reward in rewards.items()
+        )
         if self.config["normalize_reward"]:
-            reward = utils.lmap(reward,
-                                [self.config["collision_reward"],
-                                 self.config["high_speed_reward"] + self.config["right_lane_reward"]],
-                                [0, 1])
-        reward *= rewards['on_road_reward']
+            reward = utils.lmap(
+                reward,
+                [
+                    self.config["collision_reward"],
+                    self.config["high_speed_reward"] + self.config["right_lane_reward"],
+                ],
+                [0, 1],
+            )
+        reward *= rewards["on_road_reward"]
         return reward
 
     def _rewards(self, action: Action) -> Dict[Text, float]:
         neighbours = self.road.network.all_side_lanes(self.vehicle.lane_index)
-        lane = self.vehicle.target_lane_index[2] if isinstance(self.vehicle, ControlledVehicle) \
+        lane = (
+            self.vehicle.target_lane_index[2]
+            if isinstance(self.vehicle, ControlledVehicle)
             else self.vehicle.lane_index[2]
+        )
         # Use forward speed rather than speed, see https://github.com/eleurent/highway-env/issues/268
         forward_speed = self.vehicle.speed * np.cos(self.vehicle.heading)
-        scaled_speed = utils.lmap(forward_speed, self.config["reward_speed_range"], [0, 1])
+        scaled_speed = utils.lmap(
+            forward_speed, self.config["reward_speed_range"], [0, 1]
+        )
         return {
             "collision_reward": float(self.vehicle.crashed),
             "right_lane_reward": lane / max(len(neighbours) - 1, 1),
             "high_speed_reward": np.clip(scaled_speed, 0, 1),
-            "on_road_reward": float(self.vehicle.on_road)
+            "on_road_reward": float(self.vehicle.on_road),
         }
 
     def _is_terminated(self) -> bool:
         """The episode is over if the ego vehicle crashed."""
-        return (self.vehicle.crashed or
-                self.config["offroad_terminal"] and not self.vehicle.on_road)
+        return (
+            self.vehicle.crashed
+            or self.config["offroad_terminal"]
+            and not self.vehicle.on_road
+        )
 
     def _is_truncated(self) -> bool:
         """The episode is truncated if the time limit is reached."""
@@ -427,16 +452,19 @@ 
Source code for highway_env.envs.highway_env
        - fewer vehicles in the scene (and fewer lanes, shorter episode duration)
         - only check collision of controlled vehicles with others
     """
+
     @classmethod
     def default_config(cls) -> dict:
         cfg = super().default_config()
-        cfg.update({
-            "simulation_frequency": 5,
-            "lanes_count": 3,
-            "vehicles_count": 20,
-            "duration": 30,  # [s]
-            "ego_spacing": 1.5,
-        })
+        cfg.update(
+            {
+                "simulation_frequency": 5,
+                "lanes_count": 3,
+                "vehicles_count": 20,
+                "duration": 30,  # [s]
+                "ego_spacing": 1.5,
+            }
+        )
         return cfg
 
     def _create_vehicles(self) -> None:
diff --git a/main/_modules/highway_env/envs/intersection_env/index.html b/main/_modules/highway_env/envs/intersection_env/index.html
index 17cf9fe86..e4a1b2aee 100644
--- a/main/_modules/highway_env/envs/intersection_env/index.html
+++ b/main/_modules/highway_env/envs/intersection_env/index.html
@@ -291,28 +291,22 @@
           

             
             
Source code for highway_env.envs.intersection_env
-from typing import Dict, Tuple, Text
+from typing import Dict, Text, Tuple
 
 import numpy as np
 
 from highway_env import utils
 from highway_env.envs.common.abstract import AbstractEnv, MultiAgentWrapper
-from highway_env.road.lane import LineType, StraightLane, CircularLane, AbstractLane
+from highway_env.road.lane import AbstractLane, CircularLane, LineType, StraightLane
 from highway_env.road.regulation import RegulatedRoad
 from highway_env.road.road import RoadNetwork
 from highway_env.vehicle.kinematics import Vehicle
-from highway_env.vehicle.controller import ControlledVehicle
 
 
 

 [docs]
 class IntersectionEnv(AbstractEnv):
-
-    ACTIONS: Dict[int, str] = {
-        0: 'SLOWER',
-        1: 'IDLE',
-        2: 'FASTER'
-    }
+    ACTIONS: Dict[int, str] = {0: "SLOWER", 1: "IDLE", 2: "FASTER"}
     ACTIONS_INDEXES = {v: k for k, v in ACTIONS.items()}
 
 

@@ -320,88 +314,103 @@ 
Source code for highway_env.envs.intersection_env
@classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "Kinematics",
-                "vehicles_count": 15,
-                "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
-                "features_range": {
-                    "x": [-100, 100],
-                    "y": [-100, 100],
-                    "vx": [-20, 20],
-                    "vy": [-20, 20],
+        config.update(
+            {
+                "observation": {
+                    "type": "Kinematics",
+                    "vehicles_count": 15,
+                    "features": ["presence", "x", "y", "vx", "vy", "cos_h", "sin_h"],
+                    "features_range": {
+                        "x": [-100, 100],
+                        "y": [-100, 100],
+                        "vx": [-20, 20],
+                        "vy": [-20, 20],
+                    },
+                    "absolute": True,
+                    "flatten": False,
+                    "observe_intentions": False,
+                },
+                "action": {
+                    "type": "DiscreteMetaAction",
+                    "longitudinal": True,
+                    "lateral": False,
+                    "target_speeds": [0, 4.5, 9],
                 },
-                "absolute": True,
-                "flatten": False,
-                "observe_intentions": False
-            },
-            "action": {
-                "type": "DiscreteMetaAction",
-                "longitudinal": True,
-                "lateral": False,
-                "target_speeds": [0, 4.5, 9]
-            },
-            "duration": 13,  # [s]
-            "destination": "o1",
-            "controlled_vehicles": 1,
-            "initial_vehicle_count": 10,
-            "spawn_probability": 0.6,
-            "screen_width": 600,
-            "screen_height": 600,
-            "centering_position": [0.5, 0.6],
-            "scaling": 5.5 * 1.3,
-            "collision_reward": -5,
-            "high_speed_reward": 1,
-            "arrived_reward": 1,
-            "reward_speed_range": [7.0, 9.0],
-            "normalize_reward": False,
-            "offroad_terminal": False
-        })
+                "duration": 13,  # [s]
+                "destination": "o1",
+                "controlled_vehicles": 1,
+                "initial_vehicle_count": 10,
+                "spawn_probability": 0.6,
+                "screen_width": 600,
+                "screen_height": 600,
+                "centering_position": [0.5, 0.6],
+                "scaling": 5.5 * 1.3,
+                "collision_reward": -5,
+                "high_speed_reward": 1,
+                "arrived_reward": 1,
+                "reward_speed_range": [7.0, 9.0],
+                "normalize_reward": False,
+                "offroad_terminal": False,
+            }
+        )
         return config

 
 
     def _reward(self, action: int) -> float:
         """Aggregated reward, for cooperative agents."""
-        return sum(self._agent_reward(action, vehicle) for vehicle in self.controlled_vehicles
-                   ) / len(self.controlled_vehicles)
+        return sum(
+            self._agent_reward(action, vehicle) for vehicle in self.controlled_vehicles
+        ) / len(self.controlled_vehicles)
 
     def _rewards(self, action: int) -> Dict[Text, float]:
         """Multi-objective rewards, for cooperative agents."""
-        agents_rewards = [self._agent_rewards(action, vehicle) for vehicle in self.controlled_vehicles]
+        agents_rewards = [
+            self._agent_rewards(action, vehicle) for vehicle in self.controlled_vehicles
+        ]
         return {
-            name: sum(agent_rewards[name] for agent_rewards in agents_rewards) / len(agents_rewards)
+            name: sum(agent_rewards[name] for agent_rewards in agents_rewards)
+            / len(agents_rewards)
             for name in agents_rewards[0].keys()
         }
 
     def _agent_reward(self, action: int, vehicle: Vehicle) -> float:
         """Per-agent reward signal."""
         rewards = self._agent_rewards(action, vehicle)
-        reward = sum(self.config.get(name, 0) * reward for name, reward in rewards.items())
+        reward = sum(
+            self.config.get(name, 0) * reward for name, reward in rewards.items()
+        )
         reward = self.config["arrived_reward"] if rewards["arrived_reward"] else reward
         reward *= rewards["on_road_reward"]
         if self.config["normalize_reward"]:
-            reward = utils.lmap(reward, [self.config["collision_reward"], self.config["arrived_reward"]], [0, 1])
+            reward = utils.lmap(
+                reward,
+                [self.config["collision_reward"], self.config["arrived_reward"]],
+                [0, 1],
+            )
         return reward
 
     def _agent_rewards(self, action: int, vehicle: Vehicle) -> Dict[Text, float]:
         """Per-agent per-objective reward signal."""
-        scaled_speed = utils.lmap(vehicle.speed, self.config["reward_speed_range"], [0, 1])
+        scaled_speed = utils.lmap(
+            vehicle.speed, self.config["reward_speed_range"], [0, 1]
+        )
         return {
             "collision_reward": vehicle.crashed,
             "high_speed_reward": np.clip(scaled_speed, 0, 1),
             "arrived_reward": self.has_arrived(vehicle),
-            "on_road_reward": vehicle.on_road
+            "on_road_reward": vehicle.on_road,
         }
 
     def _is_terminated(self) -> bool:
-        return any(vehicle.crashed for vehicle in self.controlled_vehicles) \
-               or all(self.has_arrived(vehicle) for vehicle in self.controlled_vehicles) \
-               or (self.config["offroad_terminal"] and not self.vehicle.on_road)
+        return (
+            any(vehicle.crashed for vehicle in self.controlled_vehicles)
+            or all(self.has_arrived(vehicle) for vehicle in self.controlled_vehicles)
+            or (self.config["offroad_terminal"] and not self.vehicle.on_road)
+        )
 
     def _agent_is_terminal(self, vehicle: Vehicle) -> bool:
         """The episode is over when a collision occurs or when the access ramp has been passed."""
-        return (vehicle.crashed or
-                self.has_arrived(vehicle))
+        return vehicle.crashed or self.has_arrived(vehicle)
 
     def _is_truncated(self) -> bool:
         """The episode is truncated if the time limit is reached."""
@@ -409,8 +418,12 @@ 
Source code for highway_env.envs.intersection_env
def _info(self, obs: np.ndarray, action: int) -> dict:
         info = super()._info(obs, action)
-        info["agents_rewards"] = tuple(self._agent_reward(action, vehicle) for vehicle in self.controlled_vehicles)
-        info["agents_dones"] = tuple(self._agent_is_terminal(vehicle) for vehicle in self.controlled_vehicles)
+        info["agents_rewards"] = tuple(
+            self._agent_reward(action, vehicle) for vehicle in self.controlled_vehicles
+        )
+        info["agents_dones"] = tuple(
+            self._agent_is_terminal(vehicle) for vehicle in self.controlled_vehicles
+        )
         return info
 
     def _reset(self) -> None:
@@ -453,34 +466,87 @@ 
Source code for highway_env.envs.intersection_env
angle = np.radians(90 * corner)
             is_horizontal = corner % 2
             priority = 3 if is_horizontal else 1
-            rotation = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
+            rotation = np.array(
+                [[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]]
+            )
             # Incoming
-            start = rotation @ np.array([lane_width / 2, access_length + outer_distance])
+            start = rotation @ np.array(
+                [lane_width / 2, access_length + outer_distance]
+            )
             end = rotation @ np.array([lane_width / 2, outer_distance])
-            net.add_lane("o" + str(corner), "ir" + str(corner),
-                         StraightLane(start, end, line_types=[s, c], priority=priority, speed_limit=10))
+            net.add_lane(
+                "o" + str(corner),
+                "ir" + str(corner),
+                StraightLane(
+                    start, end, line_types=[s, c], priority=priority, speed_limit=10
+                ),
+            )
             # Right turn
             r_center = rotation @ (np.array([outer_distance, outer_distance]))
-            net.add_lane("ir" + str(corner), "il" + str((corner - 1) % 4),
-                         CircularLane(r_center, right_turn_radius, angle + np.radians(180), angle + np.radians(270),
-                                      line_types=[n, c], priority=priority, speed_limit=10))
+            net.add_lane(
+                "ir" + str(corner),
+                "il" + str((corner - 1) % 4),
+                CircularLane(
+                    r_center,
+                    right_turn_radius,
+                    angle + np.radians(180),
+                    angle + np.radians(270),
+                    line_types=[n, c],
+                    priority=priority,
+                    speed_limit=10,
+                ),
+            )
             # Left turn
-            l_center = rotation @ (np.array([-left_turn_radius + lane_width / 2, left_turn_radius - lane_width / 2]))
-            net.add_lane("ir" + str(corner), "il" + str((corner + 1) % 4),
-                         CircularLane(l_center, left_turn_radius, angle + np.radians(0), angle + np.radians(-90),
-                                      clockwise=False, line_types=[n, n], priority=priority - 1, speed_limit=10))
+            l_center = rotation @ (
+                np.array(
+                    [
+                        -left_turn_radius + lane_width / 2,
+                        left_turn_radius - lane_width / 2,
+                    ]
+                )
+            )
+            net.add_lane(
+                "ir" + str(corner),
+                "il" + str((corner + 1) % 4),
+                CircularLane(
+                    l_center,
+                    left_turn_radius,
+                    angle + np.radians(0),
+                    angle + np.radians(-90),
+                    clockwise=False,
+                    line_types=[n, n],
+                    priority=priority - 1,
+                    speed_limit=10,
+                ),
+            )
             # Straight
             start = rotation @ np.array([lane_width / 2, outer_distance])
             end = rotation @ np.array([lane_width / 2, -outer_distance])
-            net.add_lane("ir" + str(corner), "il" + str((corner + 2) % 4),
-                         StraightLane(start, end, line_types=[s, n], priority=priority, speed_limit=10))
+            net.add_lane(
+                "ir" + str(corner),
+                "il" + str((corner + 2) % 4),
+                StraightLane(
+                    start, end, line_types=[s, n], priority=priority, speed_limit=10
+                ),
+            )
             # Exit
-            start = rotation @ np.flip([lane_width / 2, access_length + outer_distance], axis=0)
+            start = rotation @ np.flip(
+                [lane_width / 2, access_length + outer_distance], axis=0
+            )
             end = rotation @ np.flip([lane_width / 2, outer_distance], axis=0)
-            net.add_lane("il" + str((corner - 1) % 4), "o" + str((corner - 1) % 4),
-                         StraightLane(end, start, line_types=[n, c], priority=priority, speed_limit=10))
-
-        road = RegulatedRoad(network=net, np_random=self.np_random, record_history=self.config["show_trajectories"])
+            net.add_lane(
+                "il" + str((corner - 1) % 4),
+                "o" + str((corner - 1) % 4),
+                StraightLane(
+                    end, start, line_types=[n, c], priority=priority, speed_limit=10
+                ),
+            )
+
+        road = RegulatedRoad(
+            network=net,
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
         self.road = road
 
     def _make_vehicles(self, n_vehicles: int = 10) -> None:
@@ -500,50 +566,80 @@ 
Source code for highway_env.envs.intersection_env
for t in range(n_vehicles - 1):
             self._spawn_vehicle(np.linspace(0, 80, n_vehicles)[t])
         for _ in range(simulation_steps):
-            [(self.road.act(), self.road.step(1 / self.config["simulation_frequency"])) for _ in range(self.config["simulation_frequency"])]
+            [
+                (
+                    self.road.act(),
+                    self.road.step(1 / self.config["simulation_frequency"]),
+                )
+                for _ in range(self.config["simulation_frequency"])
+            ]
 
         # Challenger vehicle
-        self._spawn_vehicle(60, spawn_probability=1, go_straight=True, position_deviation=0.1, speed_deviation=0)
+        self._spawn_vehicle(
+            60,
+            spawn_probability=1,
+            go_straight=True,
+            position_deviation=0.1,
+            speed_deviation=0,
+        )
 
         # Controlled vehicles
         self.controlled_vehicles = []
         for ego_id in range(0, self.config["controlled_vehicles"]):
-            ego_lane = self.road.network.get_lane(("o{}".format(ego_id % 4), "ir{}".format(ego_id % 4), 0))
-            destination = self.config["destination"] or "o" + str(self.np_random.integers(1, 4))
+            ego_lane = self.road.network.get_lane(
+                ("o{}".format(ego_id % 4), "ir{}".format(ego_id % 4), 0)
+            )
+            destination = self.config["destination"] or "o" + str(
+                self.np_random.integers(1, 4)
+            )
             ego_vehicle = self.action_type.vehicle_class(
-                             self.road,
-                             ego_lane.position(60 + 5*self.np_random.normal(1), 0),
-                             speed=ego_lane.speed_limit,
-                             heading=ego_lane.heading_at(60))
+                self.road,
+                ego_lane.position(60 + 5 * self.np_random.normal(1), 0),
+                speed=ego_lane.speed_limit,
+                heading=ego_lane.heading_at(60),
+            )
             try:
                 ego_vehicle.plan_route_to(destination)
-                ego_vehicle.speed_index = ego_vehicle.speed_to_index(ego_lane.speed_limit)
-                ego_vehicle.target_speed = ego_vehicle.index_to_speed(ego_vehicle.speed_index)
+                ego_vehicle.speed_index = ego_vehicle.speed_to_index(
+                    ego_lane.speed_limit
+                )
+                ego_vehicle.target_speed = ego_vehicle.index_to_speed(
+                    ego_vehicle.speed_index
+                )
             except AttributeError:
                 pass
 
             self.road.vehicles.append(ego_vehicle)
             self.controlled_vehicles.append(ego_vehicle)
             for v in self.road.vehicles:  # Prevent early collisions
-                if v is not ego_vehicle and np.linalg.norm(v.position - ego_vehicle.position) < 20:
+                if (
+                    v is not ego_vehicle
+                    and np.linalg.norm(v.position - ego_vehicle.position) < 20
+                ):
                     self.road.vehicles.remove(v)
 
-    def _spawn_vehicle(self,
-                       longitudinal: float = 0,
-                       position_deviation: float = 1.,
-                       speed_deviation: float = 1.,
-                       spawn_probability: float = 0.6,
-                       go_straight: bool = False) -> None:
+    def _spawn_vehicle(
+        self,
+        longitudinal: float = 0,
+        position_deviation: float = 1.0,
+        speed_deviation: float = 1.0,
+        spawn_probability: float = 0.6,
+        go_straight: bool = False,
+    ) -> None:
         if self.np_random.uniform() > spawn_probability:
             return
 
         route = self.np_random.choice(range(4), size=2, replace=False)
         route[1] = (route[0] + 2) % 4 if go_straight else route[1]
         vehicle_type = utils.class_from_path(self.config["other_vehicles_type"])
-        vehicle = vehicle_type.make_on_lane(self.road, ("o" + str(route[0]), "ir" + str(route[0]), 0),
-                                            longitudinal=(longitudinal + 5
-                                                          + self.np_random.normal() * position_deviation),
-                                            speed=8 + self.np_random.normal() * speed_deviation)
+        vehicle = vehicle_type.make_on_lane(
+            self.road,
+            ("o" + str(route[0]), "ir" + str(route[0]), 0),
+            longitudinal=(
+                longitudinal + 5 + self.np_random.normal() * position_deviation
+            ),
+            speed=8 + self.np_random.normal() * speed_deviation,
+        )
         for v in self.road.vehicles:
             if np.linalg.norm(v.position - vehicle.position) < 15:
                 return
@@ -553,16 +649,25 @@ 
Source code for highway_env.envs.intersection_env
return vehicle
 
     def _clear_vehicles(self) -> None:
-        is_leaving = lambda vehicle: "il" in vehicle.lane_index[0] and "o" in vehicle.lane_index[1] \
-                                     and vehicle.lane.local_coordinates(vehicle.position)[0] \
-                                     >= vehicle.lane.length - 4 * vehicle.LENGTH
-        self.road.vehicles = [vehicle for vehicle in self.road.vehicles if
-                              vehicle in self.controlled_vehicles or not (is_leaving(vehicle) or vehicle.route is None)]
+        is_leaving = (
+            lambda vehicle: "il" in vehicle.lane_index[0]
+            and "o" in vehicle.lane_index[1]
+            and vehicle.lane.local_coordinates(vehicle.position)[0]
+            >= vehicle.lane.length - 4 * vehicle.LENGTH
+        )
+        self.road.vehicles = [
+            vehicle
+            for vehicle in self.road.vehicles
+            if vehicle in self.controlled_vehicles
+            or not (is_leaving(vehicle) or vehicle.route is None)
+        ]
 
     def has_arrived(self, vehicle: Vehicle, exit_distance: float = 25) -> bool:
-        return "il" in vehicle.lane_index[0] \
-               and "o" in vehicle.lane_index[1] \
-               and vehicle.lane.local_coordinates(vehicle.position)[0] >= exit_distance

+        return (
+            "il" in vehicle.lane_index[0]
+            and "o" in vehicle.lane_index[1]
+            and vehicle.lane.local_coordinates(vehicle.position)[0] >= exit_distance
+        )

 
 
 
@@ -570,45 +675,58 @@ 
Source code for highway_env.envs.intersection_env
@classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "action": {
-                 "type": "MultiAgentAction",
-                 "action_config": {
-                     "type": "DiscreteMetaAction",
-                     "lateral": False,
-                     "longitudinal": True
-                 }
-            },
-            "observation": {
-                "type": "MultiAgentObservation",
-                "observation_config": {
-                    "type": "Kinematics"
-                }
-            },
-            "controlled_vehicles": 2
-        })
+        config.update(
+            {
+                "action": {
+                    "type": "MultiAgentAction",
+                    "action_config": {
+                        "type": "DiscreteMetaAction",
+                        "lateral": False,
+                        "longitudinal": True,
+                    },
+                },
+                "observation": {
+                    "type": "MultiAgentObservation",
+                    "observation_config": {"type": "Kinematics"},
+                },
+                "controlled_vehicles": 2,
+            }
+        )
         return config
 
+
 class ContinuousIntersectionEnv(IntersectionEnv):
     @classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "Kinematics",
-                "vehicles_count": 5,
-                "features": ["presence", "x", "y", "vx", "vy", "long_off", "lat_off", "ang_off"],
-            },
-            "action": {
-                "type": "ContinuousAction",
-                "steering_range": [-np.pi / 3, np.pi / 3],
-                "longitudinal": True,
-                "lateral": True,
-                "dynamical": True
-            },
-        })
+        config.update(
+            {
+                "observation": {
+                    "type": "Kinematics",
+                    "vehicles_count": 5,
+                    "features": [
+                        "presence",
+                        "x",
+                        "y",
+                        "vx",
+                        "vy",
+                        "long_off",
+                        "lat_off",
+                        "ang_off",
+                    ],
+                },
+                "action": {
+                    "type": "ContinuousAction",
+                    "steering_range": [-np.pi / 3, np.pi / 3],
+                    "longitudinal": True,
+                    "lateral": True,
+                    "dynamical": True,
+                },
+            }
+        )
         return config
 
+
 TupleMultiAgentIntersectionEnv = MultiAgentWrapper(MultiAgentIntersectionEnv)
 

           

diff --git a/main/_modules/highway_env/envs/merge_env/index.html b/main/_modules/highway_env/envs/merge_env/index.html
index 084a30d4f..a675b5685 100644
--- a/main/_modules/highway_env/envs/merge_env/index.html
+++ b/main/_modules/highway_env/envs/merge_env/index.html
@@ -297,7 +297,7 @@ 
Source code for highway_env.envs.merge_env
 
 from highway_env import utils
 from highway_env.envs.common.abstract import AbstractEnv
-from highway_env.road.lane import LineType, StraightLane, SineLane
+from highway_env.road.lane import LineType, SineLane, StraightLane
 from highway_env.road.road import Road, RoadNetwork
 from highway_env.vehicle.controller import ControlledVehicle
 from highway_env.vehicle.objects import Obstacle
@@ -320,14 +320,16 @@ 
Source code for highway_env.envs.merge_env
     @classmethod
     def default_config(cls) -> dict:
         cfg = super().default_config()
-        cfg.update({
-            "collision_reward": -1,
-            "right_lane_reward": 0.1,
-            "high_speed_reward": 0.2,
-            "reward_speed_range": [20, 30],
-            "merging_speed_reward": -0.5,
-            "lane_change_reward": -0.05,
-        })
+        cfg.update(
+            {
+                "collision_reward": -1,
+                "right_lane_reward": 0.1,
+                "high_speed_reward": 0.2,
+                "reward_speed_range": [20, 30],
+                "merging_speed_reward": -0.5,
+                "lane_change_reward": -0.05,
+            }
+        )
         return cfg

 
 
@@ -340,14 +342,23 @@ 
Source code for highway_env.envs.merge_env
         :param action: the action performed
         :return: the reward of the state-action transition
         """
-        reward = sum(self.config.get(name, 0) * reward for name, reward in self._rewards(action).items())
-        return utils.lmap(reward,
-                          [self.config["collision_reward"] + self.config["merging_speed_reward"],
-                           self.config["high_speed_reward"] + self.config["right_lane_reward"]],
-                          [0, 1])
+        reward = sum(
+            self.config.get(name, 0) * reward
+            for name, reward in self._rewards(action).items()
+        )
+        return utils.lmap(
+            reward,
+            [
+                self.config["collision_reward"] + self.config["merging_speed_reward"],
+                self.config["high_speed_reward"] + self.config["right_lane_reward"],
+            ],
+            [0, 1],
+        )
 
     def _rewards(self, action: int) -> Dict[Text, float]:
-        scaled_speed = utils.lmap(self.vehicle.speed, self.config["reward_speed_range"], [0, 1])
+        scaled_speed = utils.lmap(
+            self.vehicle.speed, self.config["reward_speed_range"], [0, 1]
+        )
         return {
             "collision_reward": self.vehicle.crashed,
             "right_lane_reward": self.vehicle.lane_index[2] / 1,
@@ -356,14 +367,15 @@ 
Source code for highway_env.envs.merge_env
             "merging_speed_reward": sum(  # Altruistic penalty
                 (vehicle.target_speed - vehicle.speed) / vehicle.target_speed
                 for vehicle in self.road.vehicles
-                if vehicle.lane_index == ("b", "c", 2) and isinstance(vehicle, ControlledVehicle)
-            )
+                if vehicle.lane_index == ("b", "c", 2)
+                and isinstance(vehicle, ControlledVehicle)
+            ),
         }
 
     def _is_terminated(self) -> bool:
         """The episode is over when a collision occurs or when the access ramp has been passed."""
         print("crash" + str(self.vehicle.crashed))
-        print("over"  + str(self.vehicle.position[0] > 370))
+        print("over" + str(self.vehicle.position[0] > 370))
         return self.vehicle.crashed or bool(self.vehicle.position[0] > 370)
 
     def _is_truncated(self) -> bool:
@@ -388,21 +400,56 @@ 
Source code for highway_env.envs.merge_env
         line_type = [[c, s], [n, c]]
         line_type_merge = [[c, s], [n, s]]
         for i in range(2):
-            net.add_lane("a", "b", StraightLane([0, y[i]], [sum(ends[:2]), y[i]], line_types=line_type[i]))
-            net.add_lane("b", "c", StraightLane([sum(ends[:2]), y[i]], [sum(ends[:3]), y[i]], line_types=line_type_merge[i]))
-            net.add_lane("c", "d", StraightLane([sum(ends[:3]), y[i]], [sum(ends), y[i]], line_types=line_type[i]))
+            net.add_lane(
+                "a",
+                "b",
+                StraightLane([0, y[i]], [sum(ends[:2]), y[i]], line_types=line_type[i]),
+            )
+            net.add_lane(
+                "b",
+                "c",
+                StraightLane(
+                    [sum(ends[:2]), y[i]],
+                    [sum(ends[:3]), y[i]],
+                    line_types=line_type_merge[i],
+                ),
+            )
+            net.add_lane(
+                "c",
+                "d",
+                StraightLane(
+                    [sum(ends[:3]), y[i]], [sum(ends), y[i]], line_types=line_type[i]
+                ),
+            )
 
         # Merging lane
         amplitude = 3.25
-        ljk = StraightLane([0, 6.5 + 4 + 4], [ends[0], 6.5 + 4 + 4], line_types=[c, c], forbidden=True)
-        lkb = SineLane(ljk.position(ends[0], -amplitude), ljk.position(sum(ends[:2]), -amplitude),
-                       amplitude, 2 * np.pi / (2*ends[1]), np.pi / 2, line_types=[c, c], forbidden=True)
-        lbc = StraightLane(lkb.position(ends[1], 0), lkb.position(ends[1], 0) + [ends[2], 0],
-                           line_types=[n, c], forbidden=True)
+        ljk = StraightLane(
+            [0, 6.5 + 4 + 4], [ends[0], 6.5 + 4 + 4], line_types=[c, c], forbidden=True
+        )
+        lkb = SineLane(
+            ljk.position(ends[0], -amplitude),
+            ljk.position(sum(ends[:2]), -amplitude),
+            amplitude,
+            2 * np.pi / (2 * ends[1]),
+            np.pi / 2,
+            line_types=[c, c],
+            forbidden=True,
+        )
+        lbc = StraightLane(
+            lkb.position(ends[1], 0),
+            lkb.position(ends[1], 0) + [ends[2], 0],
+            line_types=[n, c],
+            forbidden=True,
+        )
         net.add_lane("j", "k", ljk)
         net.add_lane("k", "b", lkb)
         net.add_lane("b", "c", lbc)
-        road = Road(network=net, np_random=self.np_random, record_history=self.config["show_trajectories"])
+        road = Road(
+            network=net,
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
         road.objects.append(Obstacle(road, lbc.position(ends[2], 0)))
         self.road = road
 
@@ -413,9 +460,9 @@ 
Source code for highway_env.envs.merge_env
         :return: the ego-vehicle
         """
         road = self.road
-        ego_vehicle = self.action_type.vehicle_class(road,
-                                                     road.network.get_lane(("a", "b", 1)).position(30, 0),
-                                                     speed=30)
+        ego_vehicle = self.action_type.vehicle_class(
+            road, road.network.get_lane(("a", "b", 1)).position(30, 0), speed=30
+        )
         road.vehicles.append(ego_vehicle)
 
         other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
@@ -426,7 +473,9 @@ 
Source code for highway_env.envs.merge_env
             speed += self.np_random.uniform(-1, 1)
             road.vehicles.append(other_vehicles_type(road, position, speed=speed))
 
-        merging_v = other_vehicles_type(road, road.network.get_lane(("j", "k", 0)).position(110, 0), speed=20)
+        merging_v = other_vehicles_type(
+            road, road.network.get_lane(("j", "k", 0)).position(110, 0), speed=20
+        )
         merging_v.target_speed = 30
         road.vehicles.append(merging_v)
         self.vehicle = ego_vehicle

diff --git a/main/_modules/highway_env/envs/parking_env/index.html b/main/_modules/highway_env/envs/parking_env/index.html
index 9a098b3c1..6af923f7e 100644
--- a/main/_modules/highway_env/envs/parking_env/index.html
+++ b/main/_modules/highway_env/envs/parking_env/index.html
@@ -294,12 +294,15 @@ 
Source code for highway_env.envs.parking_env
from abc import abstractmethod
 from typing import Optional
 
-from gymnasium import Env
 import numpy as np
+from gymnasium import Env
 
 from highway_env.envs.common.abstract import AbstractEnv
-from highway_env.envs.common.observation import MultiAgentObservation, observation_factory
-from highway_env.road.lane import StraightLane, LineType
+from highway_env.envs.common.observation import (
+    MultiAgentObservation,
+    observation_factory,
+)
+from highway_env.road.lane import LineType, StraightLane
 from highway_env.road.road import Road, RoadNetwork
 from highway_env.vehicle.graphics import VehicleGraphics
 from highway_env.vehicle.kinematics import Vehicle
@@ -325,7 +328,9 @@ 
Source code for highway_env.envs.parking_env
    """
 
     @abstractmethod
-    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: dict) -> float:
+    def compute_reward(
+        self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: dict
+    ) -> float:
         """Compute the step reward. This externalizes the reward function and makes
         it dependent on a desired goal and the one that was achieved. If you wish to include
         additional rewards that are independent of the goal, you can include the necessary values
@@ -358,12 +363,14 @@ 
Source code for highway_env.envs.parking_env
# For parking env with GrayscaleObservation, the env need
     # this PARKING_OBS to calculate the reward and the info.
     # Bug fixed by Mcfly(https://github.com/McflyWZX)
-    PARKING_OBS = {"observation": {
+    PARKING_OBS = {
+        "observation": {
             "type": "KinematicsGoal",
-            "features": ['x', 'y', 'vx', 'vy', 'cos_h', 'sin_h'],
+            "features": ["x", "y", "vx", "vy", "cos_h", "sin_h"],
             "scales": [100, 100, 5, 5, 1, 1],
-            "normalize": False
-        }}
+            "normalize": False,
+        }
+    }
 
     def __init__(self, config: dict = None, render_mode: Optional[str] = None) -> None:
         super().__init__(config, render_mode)
@@ -374,31 +381,31 @@ 
Source code for highway_env.envs.parking_env
@classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "KinematicsGoal",
-                "features": ['x', 'y', 'vx', 'vy', 'cos_h', 'sin_h'],
-                "scales": [100, 100, 5, 5, 1, 1],
-                "normalize": False
-            },
-            "action": {
-                "type": "ContinuousAction"
-            },
-            "reward_weights": [1, 0.3, 0, 0, 0.02, 0.02],
-            "success_goal_reward": 0.12,
-            "collision_reward": -5,
-            "steering_range": np.deg2rad(45),
-            "simulation_frequency": 15,
-            "policy_frequency": 5,
-            "duration": 100,
-            "screen_width": 600,
-            "screen_height": 300,
-            "centering_position": [0.5, 0.5],
-            "scaling": 7,
-            "controlled_vehicles": 1,
-            "vehicles_count": 0,
-            "add_walls": True
-        })
+        config.update(
+            {
+                "observation": {
+                    "type": "KinematicsGoal",
+                    "features": ["x", "y", "vx", "vy", "cos_h", "sin_h"],
+                    "scales": [100, 100, 5, 5, 1, 1],
+                    "normalize": False,
+                },
+                "action": {"type": "ContinuousAction"},
+                "reward_weights": [1, 0.3, 0, 0, 0.02, 0.02],
+                "success_goal_reward": 0.12,
+                "collision_reward": -5,
+                "steering_range": np.deg2rad(45),
+                "simulation_frequency": 15,
+                "policy_frequency": 5,
+                "duration": 100,
+                "screen_width": 600,
+                "screen_height": 300,
+                "centering_position": [0.5, 0.5],
+                "scaling": 7,
+                "controlled_vehicles": 1,
+                "vehicles_count": 0,
+                "add_walls": True,
+            }
+        )
         return config

 
 
@@ -409,16 +416,21 @@ 
Source code for highway_env.envs.parking_env
        Set the types and spaces of observation and action from config.
         """
         super().define_spaces()
-        self.observation_type_parking = observation_factory(self, self.PARKING_OBS["observation"])

+        self.observation_type_parking = observation_factory(
+            self, self.PARKING_OBS["observation"]
+        )

 
 
     def _info(self, obs, action) -> dict:
         info = super(ParkingEnv, self)._info(obs, action)
         if isinstance(self.observation_type, MultiAgentObservation):
-            success = tuple(self._is_success(agent_obs['achieved_goal'], agent_obs['desired_goal']) for agent_obs in obs)
+            success = tuple(
+                self._is_success(agent_obs["achieved_goal"], agent_obs["desired_goal"])
+                for agent_obs in obs
+            )
         else:
             obs = self.observation_type_parking.observe()
-            success = self._is_success(obs['achieved_goal'], obs['desired_goal'])
+            success = self._is_success(obs["achieved_goal"], obs["desired_goal"])
         info.update({"is_success": success})
         return info
 
@@ -440,12 +452,26 @@ 
Source code for highway_env.envs.parking_env
length = 8
         for k in range(spots):
             x = (k + 1 - spots // 2) * (width + x_offset) - width / 2
-            net.add_lane("a", "b", StraightLane([x, y_offset], [x, y_offset+length], width=width, line_types=lt))
-            net.add_lane("b", "c", StraightLane([x, -y_offset], [x, -y_offset-length], width=width, line_types=lt))
-
-        self.road = Road(network=net,
-                         np_random=self.np_random,
-                         record_history=self.config["show_trajectories"])
+            net.add_lane(
+                "a",
+                "b",
+                StraightLane(
+                    [x, y_offset], [x, y_offset + length], width=width, line_types=lt
+                ),
+            )
+            net.add_lane(
+                "b",
+                "c",
+                StraightLane(
+                    [x, -y_offset], [x, -y_offset - length], width=width, line_types=lt
+                ),
+            )
+
+        self.road = Road(
+            network=net,
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
 
     def _create_vehicles(self) -> None:
         """Create some new random vehicles of a given type, and add them on the road."""
@@ -454,7 +480,9 @@ 
Source code for highway_env.envs.parking_env
# Controlled vehicles
         self.controlled_vehicles = []
         for i in range(self.config["controlled_vehicles"]):
-            vehicle = self.action_type.vehicle_class(self.road, [i*20, 0], 2*np.pi*self.np_random.uniform(), 0)
+            vehicle = self.action_type.vehicle_class(
+                self.road, [i * 20, 0], 2 * np.pi * self.np_random.uniform(), 0
+            )
             vehicle.color = VehicleGraphics.EGO_COLOR
             self.road.vehicles.append(vehicle)
             self.controlled_vehicles.append(vehicle)
@@ -463,7 +491,9 @@ 
Source code for highway_env.envs.parking_env
# Goal
         lane_index = empty_spots[self.np_random.choice(np.arange(len(empty_spots)))]
         lane = self.road.network.get_lane(lane_index)
-        self.goal = Landmark(self.road, lane.position(lane.length/2, 0), heading=lane.heading)
+        self.goal = Landmark(
+            self.road, lane.position(lane.length / 2, 0), heading=lane.heading
+        )
         self.road.objects.append(self.goal)
         empty_spots.remove(lane_index)
 
@@ -477,7 +507,7 @@ 
Source code for highway_env.envs.parking_env
empty_spots.remove(lane_index)
 
         # Walls
-        if self.config['add_walls']:
+        if self.config["add_walls"]:
             width, height = 70, 42
             for y in [-height / 2, height / 2]:
                 obstacle = Obstacle(self.road, [0, y])
@@ -492,7 +522,13 @@ 
Source code for highway_env.envs.parking_env
 [docs]
-    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: dict, p: float = 0.5) -> float:
+    def compute_reward(
+        self,
+        achieved_goal: np.ndarray,
+        desired_goal: np.ndarray,
+        info: dict,
+        p: float = 0.5,
+    ) -> float:
         """
         Proximity to the goal is rewarded
 
@@ -504,25 +540,44 @@ 
Source code for highway_env.envs.parking_env
        :param p: the Lp^p norm used in the reward. Use p<1 to have high kurtosis for rewards in [0, 1]
         :return: the corresponding reward
         """
-        return -np.power(np.dot(np.abs(achieved_goal - desired_goal), np.array(self.config["reward_weights"])), p)

+        return -np.power(
+            np.dot(
+                np.abs(achieved_goal - desired_goal),
+                np.array(self.config["reward_weights"]),
+            ),
+            p,
+        )

 
 
     def _reward(self, action: np.ndarray) -> float:
         obs = self.observation_type_parking.observe()
         obs = obs if isinstance(obs, tuple) else (obs,)
-        reward = sum(self.compute_reward(agent_obs['achieved_goal'], agent_obs['desired_goal'], {}) for agent_obs in obs)
-        reward += self.config['collision_reward'] * sum(v.crashed for v in self.controlled_vehicles)
+        reward = sum(
+            self.compute_reward(
+                agent_obs["achieved_goal"], agent_obs["desired_goal"], {}
+            )
+            for agent_obs in obs
+        )
+        reward += self.config["collision_reward"] * sum(
+            v.crashed for v in self.controlled_vehicles
+        )
         return reward
 
     def _is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> bool:
-        return self.compute_reward(achieved_goal, desired_goal, {}) > -self.config["success_goal_reward"]
+        return (
+            self.compute_reward(achieved_goal, desired_goal, {})
+            > -self.config["success_goal_reward"]
+        )
 
     def _is_terminated(self) -> bool:
         """The episode is over if the ego vehicle crashed or the goal is reached or time is over."""
         crashed = any(vehicle.crashed for vehicle in self.controlled_vehicles)
         obs = self.observation_type_parking.observe()
         obs = obs if isinstance(obs, tuple) else (obs,)
-        success = all(self._is_success(agent_obs['achieved_goal'], agent_obs['desired_goal']) for agent_obs in obs)
+        success = all(
+            self._is_success(agent_obs["achieved_goal"], agent_obs["desired_goal"])
+            for agent_obs in obs
+        )
         return bool(crashed or success)
 
     def _is_truncated(self) -> bool:
diff --git a/main/_modules/highway_env/envs/racetrack_env/index.html b/main/_modules/highway_env/envs/racetrack_env/index.html
index e5364b49d..c27438074 100644
--- a/main/_modules/highway_env/envs/racetrack_env/index.html
+++ b/main/_modules/highway_env/envs/racetrack_env/index.html
@@ -291,14 +291,13 @@
           

             
             
Source code for highway_env.envs.racetrack_env
-from itertools import repeat, product
-from typing import Tuple, Dict, Text
+from typing import Dict, Text
 
 import numpy as np
 
 from highway_env import utils
 from highway_env.envs.common.abstract import AbstractEnv
-from highway_env.road.lane import LineType, StraightLane, CircularLane, SineLane
+from highway_env.road.lane import CircularLane, LineType, StraightLane
 from highway_env.road.road import Road, RoadNetwork
 from highway_env.vehicle.behavior import IDMVehicle
 
@@ -322,40 +321,44 @@ 
Source code for highway_env.envs.racetrack_env
@classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "OccupancyGrid",
-                "features": ['presence', 'on_road'],
-                "grid_size": [[-18, 18], [-18, 18]],
-                "grid_step": [3, 3],
-                "as_image": False,
-                "align_to_vehicle_axes": True
-            },
-            "action": {
-                "type": "ContinuousAction",
-                "longitudinal": False,
-                "lateral": True,
-                "target_speeds": [0, 5, 10]
-            },
-            "simulation_frequency": 15,
-            "policy_frequency": 5,
-            "duration": 300,
-            "collision_reward": -1,
-            "lane_centering_cost": 4,
-            "lane_centering_reward": 1,
-            "action_reward": -0.3,
-            "controlled_vehicles": 1,
-            "other_vehicles": 1,
-            "screen_width": 600,
-            "screen_height": 600,
-            "centering_position": [0.5, 0.5],
-        })
+        config.update(
+            {
+                "observation": {
+                    "type": "OccupancyGrid",
+                    "features": ["presence", "on_road"],
+                    "grid_size": [[-18, 18], [-18, 18]],
+                    "grid_step": [3, 3],
+                    "as_image": False,
+                    "align_to_vehicle_axes": True,
+                },
+                "action": {
+                    "type": "ContinuousAction",
+                    "longitudinal": False,
+                    "lateral": True,
+                    "target_speeds": [0, 5, 10],
+                },
+                "simulation_frequency": 15,
+                "policy_frequency": 5,
+                "duration": 300,
+                "collision_reward": -1,
+                "lane_centering_cost": 4,
+                "lane_centering_reward": 1,
+                "action_reward": -0.3,
+                "controlled_vehicles": 1,
+                "other_vehicles": 1,
+                "screen_width": 600,
+                "screen_height": 600,
+                "centering_position": [0.5, 0.5],
+            }
+        )
         return config

 
 
     def _reward(self, action: np.ndarray) -> float:
         rewards = self._rewards(action)
-        reward = sum(self.config.get(name, 0) * reward for name, reward in rewards.items())
+        reward = sum(
+            self.config.get(name, 0) * reward for name, reward in rewards.items()
+        )
         reward = utils.lmap(reward, [self.config["collision_reward"], 1], [0, 1])
         reward *= rewards["on_road_reward"]
         return reward
@@ -363,7 +366,8 @@ 
Source code for highway_env.envs.racetrack_env
def _rewards(self, action: np.ndarray) -> Dict[Text, float]:
         _, lateral = self.vehicle.lane.local_coordinates(self.vehicle.position)
         return {
-            "lane_centering_reward": 1/(1+self.config["lane_centering_cost"]*lateral**2),
+            "lane_centering_reward": 1
+            / (1 + self.config["lane_centering_cost"] * lateral**2),
             "action_reward": np.linalg.norm(action),
             "collision_reward": self.vehicle.crashed,
             "on_road_reward": self.vehicle.on_road,
@@ -386,98 +390,270 @@ 
Source code for highway_env.envs.racetrack_env
speedlimits = [None, 10, 10, 10, 10, 10, 10, 10, 10]
 
         # Initialise First Lane
-        lane = StraightLane([42, 0], [100, 0], line_types=(LineType.CONTINUOUS, LineType.STRIPED), width=5, speed_limit=speedlimits[1])
+        lane = StraightLane(
+            [42, 0],
+            [100, 0],
+            line_types=(LineType.CONTINUOUS, LineType.STRIPED),
+            width=5,
+            speed_limit=speedlimits[1],
+        )
         self.lane = lane
 
         # Add Lanes to Road Network - Straight Section
         net.add_lane("a", "b", lane)
-        net.add_lane("a", "b", StraightLane([42, 5], [100, 5], line_types=(LineType.STRIPED, LineType.CONTINUOUS), width=5, speed_limit=speedlimits[1]))
+        net.add_lane(
+            "a",
+            "b",
+            StraightLane(
+                [42, 5],
+                [100, 5],
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                width=5,
+                speed_limit=speedlimits[1],
+            ),
+        )
 
         # 2 - Circular Arc #1
         center1 = [100, -20]
         radii1 = 20
-        net.add_lane("b", "c",
-                     CircularLane(center1, radii1, np.deg2rad(90), np.deg2rad(-1), width=5,
-                                  clockwise=False, line_types=(LineType.CONTINUOUS, LineType.NONE),
-                                  speed_limit=speedlimits[2]))
-        net.add_lane("b", "c",
-                     CircularLane(center1, radii1+5, np.deg2rad(90), np.deg2rad(-1), width=5,
-                                  clockwise=False, line_types=(LineType.STRIPED, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[2]))
+        net.add_lane(
+            "b",
+            "c",
+            CircularLane(
+                center1,
+                radii1,
+                np.deg2rad(90),
+                np.deg2rad(-1),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                speed_limit=speedlimits[2],
+            ),
+        )
+        net.add_lane(
+            "b",
+            "c",
+            CircularLane(
+                center1,
+                radii1 + 5,
+                np.deg2rad(90),
+                np.deg2rad(-1),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                speed_limit=speedlimits[2],
+            ),
+        )
 
         # 3 - Vertical Straight
-        net.add_lane("c", "d", StraightLane([120, -20], [120, -30],
-                                            line_types=(LineType.CONTINUOUS, LineType.NONE), width=5,
-                                            speed_limit=speedlimits[3]))
-        net.add_lane("c", "d", StraightLane([125, -20], [125, -30],
-                                            line_types=(LineType.STRIPED, LineType.CONTINUOUS), width=5,
-                                            speed_limit=speedlimits[3]))
+        net.add_lane(
+            "c",
+            "d",
+            StraightLane(
+                [120, -20],
+                [120, -30],
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                width=5,
+                speed_limit=speedlimits[3],
+            ),
+        )
+        net.add_lane(
+            "c",
+            "d",
+            StraightLane(
+                [125, -20],
+                [125, -30],
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                width=5,
+                speed_limit=speedlimits[3],
+            ),
+        )
 
         # 4 - Circular Arc #2
         center2 = [105, -30]
         radii2 = 15
-        net.add_lane("d", "e",
-                     CircularLane(center2, radii2, np.deg2rad(0), np.deg2rad(-181), width=5,
-                                  clockwise=False, line_types=(LineType.CONTINUOUS, LineType.NONE),
-                                  speed_limit=speedlimits[4]))
-        net.add_lane("d", "e",
-                     CircularLane(center2, radii2+5, np.deg2rad(0), np.deg2rad(-181), width=5,
-                                  clockwise=False, line_types=(LineType.STRIPED, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[4]))
+        net.add_lane(
+            "d",
+            "e",
+            CircularLane(
+                center2,
+                radii2,
+                np.deg2rad(0),
+                np.deg2rad(-181),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                speed_limit=speedlimits[4],
+            ),
+        )
+        net.add_lane(
+            "d",
+            "e",
+            CircularLane(
+                center2,
+                radii2 + 5,
+                np.deg2rad(0),
+                np.deg2rad(-181),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                speed_limit=speedlimits[4],
+            ),
+        )
 
         # 5 - Circular Arc #3
         center3 = [70, -30]
         radii3 = 15
-        net.add_lane("e", "f",
-                     CircularLane(center3, radii3+5, np.deg2rad(0), np.deg2rad(136), width=5,
-                                  clockwise=True, line_types=(LineType.CONTINUOUS, LineType.STRIPED),
-                                  speed_limit=speedlimits[5]))
-        net.add_lane("e", "f",
-                     CircularLane(center3, radii3, np.deg2rad(0), np.deg2rad(137), width=5,
-                                  clockwise=True, line_types=(LineType.NONE, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[5]))
+        net.add_lane(
+            "e",
+            "f",
+            CircularLane(
+                center3,
+                radii3 + 5,
+                np.deg2rad(0),
+                np.deg2rad(136),
+                width=5,
+                clockwise=True,
+                line_types=(LineType.CONTINUOUS, LineType.STRIPED),
+                speed_limit=speedlimits[5],
+            ),
+        )
+        net.add_lane(
+            "e",
+            "f",
+            CircularLane(
+                center3,
+                radii3,
+                np.deg2rad(0),
+                np.deg2rad(137),
+                width=5,
+                clockwise=True,
+                line_types=(LineType.NONE, LineType.CONTINUOUS),
+                speed_limit=speedlimits[5],
+            ),
+        )
 
         # 6 - Slant
-        net.add_lane("f", "g", StraightLane([55.7, -15.7], [35.7, -35.7],
-                                            line_types=(LineType.CONTINUOUS, LineType.NONE), width=5,
-                                            speed_limit=speedlimits[6]))
-        net.add_lane("f", "g", StraightLane([59.3934, -19.2], [39.3934, -39.2],
-                                            line_types=(LineType.STRIPED, LineType.CONTINUOUS), width=5,
-                                            speed_limit=speedlimits[6]))
+        net.add_lane(
+            "f",
+            "g",
+            StraightLane(
+                [55.7, -15.7],
+                [35.7, -35.7],
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                width=5,
+                speed_limit=speedlimits[6],
+            ),
+        )
+        net.add_lane(
+            "f",
+            "g",
+            StraightLane(
+                [59.3934, -19.2],
+                [39.3934, -39.2],
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                width=5,
+                speed_limit=speedlimits[6],
+            ),
+        )
 
         # 7 - Circular Arc #4 - Bugs out when arc is too large, hence written in 2 sections
         center4 = [18.1, -18.1]
         radii4 = 25
-        net.add_lane("g", "h",
-                     CircularLane(center4, radii4, np.deg2rad(315), np.deg2rad(170), width=5,
-                                  clockwise=False, line_types=(LineType.CONTINUOUS, LineType.NONE),
-                                  speed_limit=speedlimits[7]))
-        net.add_lane("g", "h",
-                     CircularLane(center4, radii4+5, np.deg2rad(315), np.deg2rad(165), width=5,
-                                  clockwise=False, line_types=(LineType.STRIPED, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[7]))
-        net.add_lane("h", "i",
-                     CircularLane(center4, radii4, np.deg2rad(170), np.deg2rad(56), width=5,
-                                  clockwise=False, line_types=(LineType.CONTINUOUS, LineType.NONE),
-                                  speed_limit=speedlimits[7]))
-        net.add_lane("h", "i",
-                     CircularLane(center4, radii4+5, np.deg2rad(170), np.deg2rad(58), width=5,
-                                  clockwise=False, line_types=(LineType.STRIPED, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[7]))
+        net.add_lane(
+            "g",
+            "h",
+            CircularLane(
+                center4,
+                radii4,
+                np.deg2rad(315),
+                np.deg2rad(170),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                speed_limit=speedlimits[7],
+            ),
+        )
+        net.add_lane(
+            "g",
+            "h",
+            CircularLane(
+                center4,
+                radii4 + 5,
+                np.deg2rad(315),
+                np.deg2rad(165),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                speed_limit=speedlimits[7],
+            ),
+        )
+        net.add_lane(
+            "h",
+            "i",
+            CircularLane(
+                center4,
+                radii4,
+                np.deg2rad(170),
+                np.deg2rad(56),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.CONTINUOUS, LineType.NONE),
+                speed_limit=speedlimits[7],
+            ),
+        )
+        net.add_lane(
+            "h",
+            "i",
+            CircularLane(
+                center4,
+                radii4 + 5,
+                np.deg2rad(170),
+                np.deg2rad(58),
+                width=5,
+                clockwise=False,
+                line_types=(LineType.STRIPED, LineType.CONTINUOUS),
+                speed_limit=speedlimits[7],
+            ),
+        )
 
         # 8 - Circular Arc #5 - Reconnects to Start
         center5 = [43.2, 23.4]
         radii5 = 18.5
-        net.add_lane("i", "a",
-                     CircularLane(center5, radii5+5, np.deg2rad(240), np.deg2rad(270), width=5,
-                                  clockwise=True, line_types=(LineType.CONTINUOUS, LineType.STRIPED),
-                                  speed_limit=speedlimits[8]))
-        net.add_lane("i", "a",
-                     CircularLane(center5, radii5, np.deg2rad(238), np.deg2rad(268), width=5,
-                                  clockwise=True, line_types=(LineType.NONE, LineType.CONTINUOUS),
-                                  speed_limit=speedlimits[8]))
-
-        road = Road(network=net, np_random=self.np_random, record_history=self.config["show_trajectories"])
+        net.add_lane(
+            "i",
+            "a",
+            CircularLane(
+                center5,
+                radii5 + 5,
+                np.deg2rad(240),
+                np.deg2rad(270),
+                width=5,
+                clockwise=True,
+                line_types=(LineType.CONTINUOUS, LineType.STRIPED),
+                speed_limit=speedlimits[8],
+            ),
+        )
+        net.add_lane(
+            "i",
+            "a",
+            CircularLane(
+                center5,
+                radii5,
+                np.deg2rad(238),
+                np.deg2rad(268),
+                width=5,
+                clockwise=True,
+                line_types=(LineType.NONE, LineType.CONTINUOUS),
+                speed_limit=speedlimits[8],
+            ),
+        )
+
+        road = Road(
+            network=net,
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
         self.road = road
 
     def _make_vehicles(self) -> None:
@@ -489,32 +665,40 @@ 
Source code for highway_env.envs.racetrack_env
# Controlled vehicles
         self.controlled_vehicles = []
         for i in range(self.config["controlled_vehicles"]):
-            lane_index = ("a", "b", rng.integers(2)) if i == 0 else \
-                self.road.network.random_lane_index(rng)
-            controlled_vehicle = self.action_type.vehicle_class.make_on_lane(self.road, lane_index, speed=None,
-                                                                             longitudinal=rng.uniform(20, 50))
+            lane_index = (
+                ("a", "b", rng.integers(2))
+                if i == 0
+                else self.road.network.random_lane_index(rng)
+            )
+            controlled_vehicle = self.action_type.vehicle_class.make_on_lane(
+                self.road, lane_index, speed=None, longitudinal=rng.uniform(20, 50)
+            )
 
             self.controlled_vehicles.append(controlled_vehicle)
             self.road.vehicles.append(controlled_vehicle)
 
         # Front vehicle
-        vehicle = IDMVehicle.make_on_lane(self.road, ("b", "c", lane_index[-1]),
-                                          longitudinal=rng.uniform(
-                                              low=0,
-                                              high=self.road.network.get_lane(("b", "c", 0)).length
-                                          ),
-                                          speed=6+rng.uniform(high=3))
+        vehicle = IDMVehicle.make_on_lane(
+            self.road,
+            ("b", "c", lane_index[-1]),
+            longitudinal=rng.uniform(
+                low=0, high=self.road.network.get_lane(("b", "c", 0)).length
+            ),
+            speed=6 + rng.uniform(high=3),
+        )
         self.road.vehicles.append(vehicle)
 
         # Other vehicles
         for i in range(rng.integers(self.config["other_vehicles"])):
             random_lane_index = self.road.network.random_lane_index(rng)
-            vehicle = IDMVehicle.make_on_lane(self.road, random_lane_index,
-                                              longitudinal=rng.uniform(
-                                                  low=0,
-                                                  high=self.road.network.get_lane(random_lane_index).length
-                                              ),
-                                              speed=6+rng.uniform(high=3))
+            vehicle = IDMVehicle.make_on_lane(
+                self.road,
+                random_lane_index,
+                longitudinal=rng.uniform(
+                    low=0, high=self.road.network.get_lane(random_lane_index).length
+                ),
+                speed=6 + rng.uniform(high=3),
+            )
             # Prevent early collisions
             for v in self.road.vehicles:
                 if np.linalg.norm(vehicle.position - v.position) < 20:
diff --git a/main/_modules/highway_env/envs/roundabout_env/index.html b/main/_modules/highway_env/envs/roundabout_env/index.html
index 3a57c6659..3cfc3332e 100644
--- a/main/_modules/highway_env/envs/roundabout_env/index.html
+++ b/main/_modules/highway_env/envs/roundabout_env/index.html
@@ -291,13 +291,13 @@
           

             
             
Source code for highway_env.envs.roundabout_env
-from typing import Tuple, Dict, Text
+from typing import Dict, Text
 
 import numpy as np
 
 from highway_env import utils
 from highway_env.envs.common.abstract import AbstractEnv
-from highway_env.road.lane import LineType, StraightLane, CircularLane, SineLane
+from highway_env.road.lane import CircularLane, LineType, SineLane, StraightLane
 from highway_env.road.road import Road, RoadNetwork
 from highway_env.vehicle.controller import MDPVehicle
 
@@ -305,51 +305,60 @@ 
Source code for highway_env.envs.roundabout_env
<
 

 [docs]
 class RoundaboutEnv(AbstractEnv):
-
 

 [docs]
     @classmethod
     def default_config(cls) -> dict:
         config = super().default_config()
-        config.update({
-            "observation": {
-                "type": "Kinematics",
-                "absolute": True,
-                "features_range": {"x": [-100, 100], "y": [-100, 100], "vx": [-15, 15], "vy": [-15, 15]},
-            },
-            "action": {
-                "type": "DiscreteMetaAction",
-                "target_speeds": [0, 8, 16]
-            },
-            "incoming_vehicle_destination": None,
-            "collision_reward": -1,
-            "high_speed_reward": 0.2,
-            "right_lane_reward": 0,
-            "lane_change_reward": -0.05,
-            "screen_width": 600,
-            "screen_height": 600,
-            "centering_position": [0.5, 0.6],
-            "duration": 11,
-            "normalize_reward": True
-        })
+        config.update(
+            {
+                "observation": {
+                    "type": "Kinematics",
+                    "absolute": True,
+                    "features_range": {
+                        "x": [-100, 100],
+                        "y": [-100, 100],
+                        "vx": [-15, 15],
+                        "vy": [-15, 15],
+                    },
+                },
+                "action": {"type": "DiscreteMetaAction", "target_speeds": [0, 8, 16]},
+                "incoming_vehicle_destination": None,
+                "collision_reward": -1,
+                "high_speed_reward": 0.2,
+                "right_lane_reward": 0,
+                "lane_change_reward": -0.05,
+                "screen_width": 600,
+                "screen_height": 600,
+                "centering_position": [0.5, 0.6],
+                "duration": 11,
+                "normalize_reward": True,
+            }
+        )
         return config

 
 
     def _reward(self, action: int) -> float:
         rewards = self._rewards(action)
-        reward = sum(self.config.get(name, 0) * reward for name, reward in rewards.items())
+        reward = sum(
+            self.config.get(name, 0) * reward for name, reward in rewards.items()
+        )
         if self.config["normalize_reward"]:
-            reward = utils.lmap(reward, [self.config["collision_reward"], self.config["high_speed_reward"]], [0, 1])
+            reward = utils.lmap(
+                reward,
+                [self.config["collision_reward"], self.config["high_speed_reward"]],
+                [0, 1],
+            )
         reward *= rewards["on_road_reward"]
         return reward
 
     def _rewards(self, action: int) -> Dict[Text, float]:
         return {
             "collision_reward": self.vehicle.crashed,
-            "high_speed_reward":
-                 MDPVehicle.get_speed_index(self.vehicle) / (MDPVehicle.DEFAULT_TARGET_SPEEDS.size - 1),
+            "high_speed_reward": MDPVehicle.get_speed_index(self.vehicle)
+            / (MDPVehicle.DEFAULT_TARGET_SPEEDS.size - 1),
             "lane_change_reward": action in [0, 2],
-            "on_road_reward": self.vehicle.on_road
+            "on_road_reward": self.vehicle.on_road,
         }
 
     def _is_terminated(self) -> bool:
@@ -369,64 +378,244 @@ 
Source code for highway_env.envs.roundabout_env
<
         alpha = 24  # [deg]
 
         net = RoadNetwork()
-        radii = [radius, radius+4]
+        radii = [radius, radius + 4]
         n, c, s = LineType.NONE, LineType.CONTINUOUS, LineType.STRIPED
         line = [[c, s], [n, c]]
         for lane in [0, 1]:
-            net.add_lane("se", "ex",
-                         CircularLane(center, radii[lane], np.deg2rad(90 - alpha), np.deg2rad(alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("ex", "ee",
-                         CircularLane(center, radii[lane], np.deg2rad(alpha), np.deg2rad(-alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("ee", "nx",
-                         CircularLane(center, radii[lane], np.deg2rad(-alpha), np.deg2rad(-90 + alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("nx", "ne",
-                         CircularLane(center, radii[lane], np.deg2rad(-90 + alpha), np.deg2rad(-90 - alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("ne", "wx",
-                         CircularLane(center, radii[lane], np.deg2rad(-90 - alpha), np.deg2rad(-180 + alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("wx", "we",
-                         CircularLane(center, radii[lane], np.deg2rad(-180 + alpha), np.deg2rad(-180 - alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("we", "sx",
-                         CircularLane(center, radii[lane], np.deg2rad(180 - alpha), np.deg2rad(90 + alpha),
-                                      clockwise=False, line_types=line[lane]))
-            net.add_lane("sx", "se",
-                         CircularLane(center, radii[lane], np.deg2rad(90 + alpha), np.deg2rad(90 - alpha),
-                                      clockwise=False, line_types=line[lane]))
+            net.add_lane(
+                "se",
+                "ex",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(90 - alpha),
+                    np.deg2rad(alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "ex",
+                "ee",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(alpha),
+                    np.deg2rad(-alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "ee",
+                "nx",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(-alpha),
+                    np.deg2rad(-90 + alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "nx",
+                "ne",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(-90 + alpha),
+                    np.deg2rad(-90 - alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "ne",
+                "wx",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(-90 - alpha),
+                    np.deg2rad(-180 + alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "wx",
+                "we",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(-180 + alpha),
+                    np.deg2rad(-180 - alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "we",
+                "sx",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(180 - alpha),
+                    np.deg2rad(90 + alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
+            net.add_lane(
+                "sx",
+                "se",
+                CircularLane(
+                    center,
+                    radii[lane],
+                    np.deg2rad(90 + alpha),
+                    np.deg2rad(90 - alpha),
+                    clockwise=False,
+                    line_types=line[lane],
+                ),
+            )
 
         # Access lanes: (r)oad/(s)ine
         access = 170  # [m]
         dev = 85  # [m]
         a = 5  # [m]
-        delta_st = 0.2*dev  # [m]
-
-        delta_en = dev-delta_st
-        w = 2*np.pi/dev
-        net.add_lane("ser", "ses", StraightLane([2, access], [2, dev/2], line_types=(s, c)))
-        net.add_lane("ses", "se", SineLane([2+a, dev/2], [2+a, dev/2-delta_st], a, w, -np.pi/2, line_types=(c, c)))
-        net.add_lane("sx", "sxs", SineLane([-2-a, -dev/2+delta_en], [-2-a, dev/2], a, w, -np.pi/2+w*delta_en, line_types=(c, c)))
-        net.add_lane("sxs", "sxr", StraightLane([-2, dev / 2], [-2, access], line_types=(n, c)))
-
-        net.add_lane("eer", "ees", StraightLane([access, -2], [dev / 2, -2], line_types=(s, c)))
-        net.add_lane("ees", "ee", SineLane([dev / 2, -2-a], [dev / 2 - delta_st, -2-a], a, w, -np.pi / 2, line_types=(c, c)))
-        net.add_lane("ex", "exs", SineLane([-dev / 2 + delta_en, 2+a], [dev / 2, 2+a], a, w, -np.pi / 2 + w * delta_en, line_types=(c, c)))
-        net.add_lane("exs", "exr", StraightLane([dev / 2, 2], [access, 2], line_types=(n, c)))
-
-        net.add_lane("ner", "nes", StraightLane([-2, -access], [-2, -dev / 2], line_types=(s, c)))
-        net.add_lane("nes", "ne", SineLane([-2 - a, -dev / 2], [-2 - a, -dev / 2 + delta_st], a, w, -np.pi / 2, line_types=(c, c)))
-        net.add_lane("nx", "nxs", SineLane([2 + a, dev / 2 - delta_en], [2 + a, -dev / 2], a, w, -np.pi / 2 + w * delta_en, line_types=(c, c)))
-        net.add_lane("nxs", "nxr", StraightLane([2, -dev / 2], [2, -access], line_types=(n, c)))
-
-        net.add_lane("wer", "wes", StraightLane([-access, 2], [-dev / 2, 2], line_types=(s, c)))
-        net.add_lane("wes", "we", SineLane([-dev / 2, 2+a], [-dev / 2 + delta_st, 2+a], a, w, -np.pi / 2, line_types=(c, c)))
-        net.add_lane("wx", "wxs", SineLane([dev / 2 - delta_en, -2-a], [-dev / 2, -2-a], a, w, -np.pi / 2 + w * delta_en, line_types=(c, c)))
-        net.add_lane("wxs", "wxr", StraightLane([-dev / 2, -2], [-access, -2], line_types=(n, c)))
-
-        road = Road(network=net, np_random=self.np_random, record_history=self.config["show_trajectories"])
+        delta_st = 0.2 * dev  # [m]
+
+        delta_en = dev - delta_st
+        w = 2 * np.pi / dev
+        net.add_lane(
+            "ser", "ses", StraightLane([2, access], [2, dev / 2], line_types=(s, c))
+        )
+        net.add_lane(
+            "ses",
+            "se",
+            SineLane(
+                [2 + a, dev / 2],
+                [2 + a, dev / 2 - delta_st],
+                a,
+                w,
+                -np.pi / 2,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "sx",
+            "sxs",
+            SineLane(
+                [-2 - a, -dev / 2 + delta_en],
+                [-2 - a, dev / 2],
+                a,
+                w,
+                -np.pi / 2 + w * delta_en,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "sxs", "sxr", StraightLane([-2, dev / 2], [-2, access], line_types=(n, c))
+        )
+
+        net.add_lane(
+            "eer", "ees", StraightLane([access, -2], [dev / 2, -2], line_types=(s, c))
+        )
+        net.add_lane(
+            "ees",
+            "ee",
+            SineLane(
+                [dev / 2, -2 - a],
+                [dev / 2 - delta_st, -2 - a],
+                a,
+                w,
+                -np.pi / 2,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "ex",
+            "exs",
+            SineLane(
+                [-dev / 2 + delta_en, 2 + a],
+                [dev / 2, 2 + a],
+                a,
+                w,
+                -np.pi / 2 + w * delta_en,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "exs", "exr", StraightLane([dev / 2, 2], [access, 2], line_types=(n, c))
+        )
+
+        net.add_lane(
+            "ner", "nes", StraightLane([-2, -access], [-2, -dev / 2], line_types=(s, c))
+        )
+        net.add_lane(
+            "nes",
+            "ne",
+            SineLane(
+                [-2 - a, -dev / 2],
+                [-2 - a, -dev / 2 + delta_st],
+                a,
+                w,
+                -np.pi / 2,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "nx",
+            "nxs",
+            SineLane(
+                [2 + a, dev / 2 - delta_en],
+                [2 + a, -dev / 2],
+                a,
+                w,
+                -np.pi / 2 + w * delta_en,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "nxs", "nxr", StraightLane([2, -dev / 2], [2, -access], line_types=(n, c))
+        )
+
+        net.add_lane(
+            "wer", "wes", StraightLane([-access, 2], [-dev / 2, 2], line_types=(s, c))
+        )
+        net.add_lane(
+            "wes",
+            "we",
+            SineLane(
+                [-dev / 2, 2 + a],
+                [-dev / 2 + delta_st, 2 + a],
+                a,
+                w,
+                -np.pi / 2,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "wx",
+            "wxs",
+            SineLane(
+                [dev / 2 - delta_en, -2 - a],
+                [-dev / 2, -2 - a],
+                a,
+                w,
+                -np.pi / 2 + w * delta_en,
+                line_types=(c, c),
+            ),
+        )
+        net.add_lane(
+            "wxs", "wxr", StraightLane([-dev / 2, -2], [-access, -2], line_types=(n, c))
+        )
+
+        road = Road(
+            network=net,
+            np_random=self.np_random,
+            record_history=self.config["show_trajectories"],
+        )
         self.road = road
 
     def _make_vehicles(self) -> None:
@@ -440,10 +629,12 @@ 
Source code for highway_env.envs.roundabout_env
<
 
         # Ego-vehicle
         ego_lane = self.road.network.get_lane(("ser", "ses", 0))
-        ego_vehicle = self.action_type.vehicle_class(self.road,
-                                                     ego_lane.position(125, 0),
-                                                     speed=8,
-                                                     heading=ego_lane.heading_at(140))
+        ego_vehicle = self.action_type.vehicle_class(
+            self.road,
+            ego_lane.position(125, 0),
+            speed=8,
+            heading=ego_lane.heading_at(140),
+        )
         try:
             ego_vehicle.plan_route_to("nxs")
         except AttributeError:
@@ -454,10 +645,12 @@ 
Source code for highway_env.envs.roundabout_env
<
         # Incoming vehicle
         destinations = ["exr", "sxr", "nxr"]
         other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
-        vehicle = other_vehicles_type.make_on_lane(self.road,
-                                                   ("we", "sx", 1),
-                                                   longitudinal=5 + self.np_random.normal()*position_deviation,
-                                                   speed=16 + self.np_random.normal() * speed_deviation)
+        vehicle = other_vehicles_type.make_on_lane(
+            self.road,
+            ("we", "sx", 1),
+            longitudinal=5 + self.np_random.normal() * position_deviation,
+            speed=16 + self.np_random.normal() * speed_deviation,
+        )
 
         if self.config["incoming_vehicle_destination"] is not None:
             destination = destinations[self.config["incoming_vehicle_destination"]]
@@ -469,19 +662,23 @@ 
Source code for highway_env.envs.roundabout_env
<
 
         # Other vehicles
         for i in list(range(1, 2)) + list(range(-1, 0)):
-            vehicle = other_vehicles_type.make_on_lane(self.road,
-                                                       ("we", "sx", 0),
-                                                       longitudinal=20*i + self.np_random.normal()*position_deviation,
-                                                       speed=16 + self.np_random.normal() * speed_deviation)
+            vehicle = other_vehicles_type.make_on_lane(
+                self.road,
+                ("we", "sx", 0),
+                longitudinal=20 * i + self.np_random.normal() * position_deviation,
+                speed=16 + self.np_random.normal() * speed_deviation,
+            )
             vehicle.plan_route_to(self.np_random.choice(destinations))
             vehicle.randomize_behavior()
             self.road.vehicles.append(vehicle)
 
         # Entering vehicle
-        vehicle = other_vehicles_type.make_on_lane(self.road,
-                                                   ("eer", "ees", 0),
-                                                   longitudinal=50 + self.np_random.normal() * position_deviation,
-                                                   speed=16 + self.np_random.normal() * speed_deviation)
+        vehicle = other_vehicles_type.make_on_lane(
+            self.road,
+            ("eer", "ees", 0),
+            longitudinal=50 + self.np_random.normal() * position_deviation,
+            speed=16 + self.np_random.normal() * speed_deviation,
+        )
         vehicle.plan_route_to(self.np_random.choice(destinations))
         vehicle.randomize_behavior()
         self.road.vehicles.append(vehicle)

diff --git a/main/_modules/highway_env/road/graphics/index.html b/main/_modules/highway_env/road/graphics/index.html
index df859a6f1..4c5559c42 100644
--- a/main/_modules/highway_env/road/graphics/index.html
+++ b/main/_modules/highway_env/road/graphics/index.html
@@ -291,16 +291,16 @@
           

             
             
Source code for highway_env.road.graphics
-from typing import List, Tuple, Union, TYPE_CHECKING
+from typing import TYPE_CHECKING, List, Tuple, Union
 
 import numpy as np
 import pygame
 
-from highway_env.road.lane import LineType, AbstractLane
+from highway_env.road.lane import AbstractLane, LineType
 from highway_env.road.road import Road
 from highway_env.utils import Vector
 from highway_env.vehicle.graphics import VehicleGraphics
-from highway_env.vehicle.objects import Obstacle, Landmark
+from highway_env.vehicle.objects import Landmark, Obstacle
 
 if TYPE_CHECKING:
     from highway_env.vehicle.objects import RoadObject
@@ -324,7 +324,9 @@ 
Source code for highway_env.road.graphics
     SCALING_FACTOR = 1.3
     MOVING_FACTOR = 0.1
 
-    def __init__(self, size: Tuple[int, int], flags: object, surf: pygame.SurfaceType) -> None:
+    def __init__(
+        self, size: Tuple[int, int], flags: object, surf: pygame.SurfaceType
+    ) -> None:
         super().__init__(size, flags, surf)
         self.origin = np.array([0, 0])
         self.scaling = self.INITIAL_SCALING
@@ -377,7 +379,10 @@ 
Source code for highway_env.road.graphics
         :return: whether the position is visible
         """
         x, y = self.vec2pix(vec)
-        return -margin < x < self.get_width() + margin and -margin < y < self.get_height() + margin

+        return (
+            -margin < x < self.get_width() + margin
+            and -margin < y < self.get_height() + margin
+        )

 
 
 

@@ -389,8 +394,11 @@ 
Source code for highway_env.road.graphics
         :param position: a world position [m]
         """
         self.origin = position - np.array(
-            [self.centering_position[0] * self.get_width() / self.scaling,
-             self.centering_position[1] * self.get_height() / self.scaling])

+            [
+                self.centering_position[0] * self.get_width() / self.scaling,
+                self.centering_position[1] * self.get_height() / self.scaling,
+            ]
+        )

 
 
 

@@ -440,9 +448,15 @@ 
Source code for highway_env.road.graphics
         :param lane: the lane to be displayed
         :param surface: the pygame surface
         """
-        stripes_count = int(2 * (surface.get_height() + surface.get_width()) / (cls.STRIPE_SPACING * surface.scaling))
+        stripes_count = int(
+            2
+            * (surface.get_height() + surface.get_width())
+            / (cls.STRIPE_SPACING * surface.scaling)
+        )
         s_origin, _ = lane.local_coordinates(surface.origin)
-        s0 = (int(s_origin) // cls.STRIPE_SPACING - stripes_count // 2) * cls.STRIPE_SPACING
+        s0 = (
+            int(s_origin) // cls.STRIPE_SPACING - stripes_count // 2
+        ) * cls.STRIPE_SPACING
         for side in range(2):
             if lane.line_types[side] == LineType.STRIPED:
                 cls.striped_line(lane, surface, stripes_count, s0, side)
@@ -455,8 +469,14 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @classmethod
-    def striped_line(cls, lane: AbstractLane, surface: WorldSurface, stripes_count: int, longitudinal: float,
-                     side: int) -> None:
+    def striped_line(
+        cls,
+        lane: AbstractLane,
+        surface: WorldSurface,
+        stripes_count: int,
+        longitudinal: float,
+        side: int,
+    ) -> None:
         """
         Draw a striped line on one side of a lane, on a surface.
 
@@ -467,7 +487,11 @@ 
Source code for highway_env.road.graphics
         :param side: which side of the road to draw [0:left, 1:right]
         """
         starts = longitudinal + np.arange(stripes_count) * cls.STRIPE_SPACING
-        ends = longitudinal + np.arange(stripes_count) * cls.STRIPE_SPACING + cls.STRIPE_LENGTH
+        ends = (
+            longitudinal
+            + np.arange(stripes_count) * cls.STRIPE_SPACING
+            + cls.STRIPE_LENGTH
+        )
         lats = [(side - 0.5) * lane.width_at(s) for s in starts]
         cls.draw_stripes(lane, surface, starts, ends, lats)

 
@@ -475,8 +499,14 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @classmethod
-    def continuous_curve(cls, lane: AbstractLane, surface: WorldSurface, stripes_count: int,
-                         longitudinal: float, side: int) -> None:
+    def continuous_curve(
+        cls,
+        lane: AbstractLane,
+        surface: WorldSurface,
+        stripes_count: int,
+        longitudinal: float,
+        side: int,
+    ) -> None:
         """
         Draw a striped line on one side of a lane, on a surface.
 
@@ -487,7 +517,11 @@ 
Source code for highway_env.road.graphics
         :param side: which side of the road to draw [0:left, 1:right]
         """
         starts = longitudinal + np.arange(stripes_count) * cls.STRIPE_SPACING
-        ends = longitudinal + np.arange(stripes_count) * cls.STRIPE_SPACING + cls.STRIPE_SPACING
+        ends = (
+            longitudinal
+            + np.arange(stripes_count) * cls.STRIPE_SPACING
+            + cls.STRIPE_SPACING
+        )
         lats = [(side - 0.5) * lane.width_at(s) for s in starts]
         cls.draw_stripes(lane, surface, starts, ends, lats)

 
@@ -495,8 +529,14 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @classmethod
-    def continuous_line(cls, lane: AbstractLane, surface: WorldSurface, stripes_count: int, longitudinal: float,
-                        side: int) -> None:
+    def continuous_line(
+        cls,
+        lane: AbstractLane,
+        surface: WorldSurface,
+        stripes_count: int,
+        longitudinal: float,
+        side: int,
+    ) -> None:
         """
         Draw a continuous line on one side of a lane, on a surface.
 
@@ -515,8 +555,14 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @classmethod
-    def draw_stripes(cls, lane: AbstractLane, surface: WorldSurface,
-                     starts: List[float], ends: List[float], lats: List[float]) -> None:
+    def draw_stripes(
+        cls,
+        lane: AbstractLane,
+        surface: WorldSurface,
+        starts: List[float],
+        ends: List[float],
+        lats: List[float],
+    ) -> None:
         """
         Draw a set of stripes along a lane.
 
@@ -530,24 +576,44 @@ 
Source code for highway_env.road.graphics
         ends = np.clip(ends, 0, lane.length)
         for k, _ in enumerate(starts):
             if abs(starts[k] - ends[k]) > 0.5 * cls.STRIPE_LENGTH:
-                pygame.draw.line(surface, surface.WHITE,
-                                 (surface.vec2pix(lane.position(starts[k], lats[k]))),
-                                 (surface.vec2pix(lane.position(ends[k], lats[k]))),
-                                 max(surface.pix(cls.STRIPE_WIDTH), 1))

+                pygame.draw.line(
+                    surface,
+                    surface.WHITE,
+                    (surface.vec2pix(lane.position(starts[k], lats[k]))),
+                    (surface.vec2pix(lane.position(ends[k], lats[k]))),
+                    max(surface.pix(cls.STRIPE_WIDTH), 1),
+                )

 
 
     @classmethod
-    def draw_ground(cls, lane: AbstractLane, surface: WorldSurface, color: Tuple[float], width: float,
-                    draw_surface: pygame.Surface = None) -> None:
+    def draw_ground(
+        cls,
+        lane: AbstractLane,
+        surface: WorldSurface,
+        color: Tuple[float],
+        width: float,
+        draw_surface: pygame.Surface = None,
+    ) -> None:
         draw_surface = draw_surface or surface
-        stripes_count = int(2 * (surface.get_height() + surface.get_width()) / (cls.STRIPE_SPACING * surface.scaling))
+        stripes_count = int(
+            2
+            * (surface.get_height() + surface.get_width())
+            / (cls.STRIPE_SPACING * surface.scaling)
+        )
         s_origin, _ = lane.local_coordinates(surface.origin)
-        s0 = (int(s_origin) // cls.STRIPE_SPACING - stripes_count // 2) * cls.STRIPE_SPACING
+        s0 = (
+            int(s_origin) // cls.STRIPE_SPACING - stripes_count // 2
+        ) * cls.STRIPE_SPACING
         dots = []
         for side in range(2):
-            longis = np.clip(s0 + np.arange(stripes_count) * cls.STRIPE_SPACING, 0, lane.length)
+            longis = np.clip(
+                s0 + np.arange(stripes_count) * cls.STRIPE_SPACING, 0, lane.length
+            )
             lats = [2 * (side - 0.5) * width for _ in longis]
-            new_dots = [surface.vec2pix(lane.position(longi, lat)) for longi, lat in zip(longis, lats)]
+            new_dots = [
+                surface.vec2pix(lane.position(longi, lat))
+                for longi, lat in zip(longis, lats)
+            ]
             new_dots = reversed(new_dots) if side else new_dots
             dots.extend(new_dots)
         pygame.draw.polygon(draw_surface, color, dots, 0)

@@ -580,8 +646,12 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @staticmethod
-    def display_traffic(road: Road, surface: WorldSurface, simulation_frequency: int = 15, offscreen: bool = False) \
-            -> None:
+    def display_traffic(
+        road: Road,
+        surface: WorldSurface,
+        simulation_frequency: int = 15,
+        offscreen: bool = False,
+    ) -> None:
         """
         Display the road vehicles on a surface.
 
@@ -592,7 +662,9 @@ 
Source code for highway_env.road.graphics
         """
         if road.record_history:
             for v in road.vehicles:
-                VehicleGraphics.display_history(v, surface, simulation=simulation_frequency, offscreen=offscreen)
+                VehicleGraphics.display_history(
+                    v, surface, simulation=simulation_frequency, offscreen=offscreen
+                )
         for v in road.vehicles:
             VehicleGraphics.display(v, surface, offscreen=offscreen)

 
@@ -600,7 +672,9 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @staticmethod
-    def display_road_objects(road: Road, surface: WorldSurface, offscreen: bool = False) -> None:
+    def display_road_objects(
+        road: Road, surface: WorldSurface, offscreen: bool = False
+    ) -> None:
         """
         Display the road objects on a surface.
 
@@ -630,8 +704,13 @@ 
Source code for highway_env.road.graphics
 

 [docs]
     @classmethod
-    def display(cls, object_: 'RoadObject', surface: WorldSurface, transparent: bool = False,
-                offscreen: bool = False):
+    def display(
+        cls,
+        object_: "RoadObject",
+        surface: WorldSurface,
+        transparent: bool = False,
+        offscreen: bool = False,
+    ):
         """
         Display a road objects on a pygame surface.
 
@@ -643,30 +722,51 @@ 
Source code for highway_env.road.graphics
         :param offscreen: whether the rendering should be done offscreen or not
         """
         o = object_
-        s = pygame.Surface((surface.pix(o.LENGTH), surface.pix(o.LENGTH)), pygame.SRCALPHA)  # per-pixel alpha
-        rect = (0, surface.pix(o.LENGTH / 2 - o.WIDTH / 2), surface.pix(o.LENGTH), surface.pix(o.WIDTH))
+        s = pygame.Surface(
+            (surface.pix(o.LENGTH), surface.pix(o.LENGTH)), pygame.SRCALPHA
+        )  # per-pixel alpha
+        rect = (
+            0,
+            surface.pix(o.LENGTH / 2 - o.WIDTH / 2),
+            surface.pix(o.LENGTH),
+            surface.pix(o.WIDTH),
+        )
         pygame.draw.rect(s, cls.get_color(o, transparent), rect, 0)
         pygame.draw.rect(s, cls.BLACK, rect, 1)
-        if not offscreen:  # convert_alpha throws errors in offscreen mode TODO() Explain why
+        if (
+            not offscreen
+        ):  # convert_alpha throws errors in offscreen mode TODO() Explain why
             s = pygame.Surface.convert_alpha(s)
         h = o.heading if abs(o.heading) > 2 * np.pi / 180 else 0
         # Centered rotation
-        position = (surface.pos2pix(o.position[0], o.position[1]))
+        position = surface.pos2pix(o.position[0], o.position[1])
         cls.blit_rotate(surface, s, position, np.rad2deg(-h))

 
 
 

 [docs]
     @staticmethod
-    def blit_rotate(surf: pygame.SurfaceType, image: pygame.SurfaceType, pos: Vector, angle: float,
-                    origin_pos: Vector = None, show_rect: bool = False) -> None:
+    def blit_rotate(
+        surf: pygame.SurfaceType,
+        image: pygame.SurfaceType,
+        pos: Vector,
+        angle: float,
+        origin_pos: Vector = None,
+        show_rect: bool = False,
+    ) -> None:
         """Many thanks to https://stackoverflow.com/a/54714144."""
         # calculate the axis aligned bounding box of the rotated image
         w, h = image.get_size()
         box = [pygame.math.Vector2(p) for p in [(0, 0), (w, 0), (w, -h), (0, -h)]]
         box_rotate = [p.rotate(angle) for p in box]
-        min_box = (min(box_rotate, key=lambda p: p[0])[0], min(box_rotate, key=lambda p: p[1])[1])
-        max_box = (max(box_rotate, key=lambda p: p[0])[0], max(box_rotate, key=lambda p: p[1])[1])
+        min_box = (
+            min(box_rotate, key=lambda p: p[0])[0],
+            min(box_rotate, key=lambda p: p[1])[1],
+        )
+        max_box = (
+            max(box_rotate, key=lambda p: p[0])[0],
+            max(box_rotate, key=lambda p: p[1])[1],
+        )
 
         # calculate the translation of the pivot
         if origin_pos is None:
@@ -676,8 +776,10 @@ 
Source code for highway_env.road.graphics
         pivot_move = pivot_rotate - pivot
 
         # calculate the upper left origin of the rotated image
-        origin = (pos[0] - origin_pos[0] + min_box[0] - pivot_move[0],
-                  pos[1] - origin_pos[1] - max_box[1] + pivot_move[1])
+        origin = (
+            pos[0] - origin_pos[0] + min_box[0] - pivot_move[0],
+            pos[1] - origin_pos[1] - max_box[1] + pivot_move[1],
+        )
         # get a rotated image
         rotated_image = pygame.transform.rotate(image, angle)
         # rotate and blit the image
@@ -688,7 +790,7 @@ 
Source code for highway_env.road.graphics
 
 
     @classmethod
-    def get_color(cls, object_: 'RoadObject', transparent: bool = False):
+    def get_color(cls, object_: "RoadObject", transparent: bool = False):
         color = cls.DEFAULT_COLOR
 
         if isinstance(object_, Obstacle):
diff --git a/main/_modules/highway_env/road/lane/index.html b/main/_modules/highway_env/road/lane/index.html
index 1ef36e035..16f1a748b 100644
--- a/main/_modules/highway_env/road/lane/index.html
+++ b/main/_modules/highway_env/road/lane/index.html
@@ -292,13 +292,13 @@
             
             
Source code for highway_env.road.lane
 from abc import ABCMeta, abstractmethod
-from copy import deepcopy
-from typing import Tuple, List, Optional, Union
+from typing import List, Optional, Tuple, Union
+
 import numpy as np
 
 from highway_env import utils
 from highway_env.road.spline import LinearSpline2D
-from highway_env.utils import wrap_to_pi, Vector, get_class_path, class_from_path
+from highway_env.utils import Vector, class_from_path, get_class_path, wrap_to_pi
 
 
 

@@ -392,8 +392,13 @@ 
Source code for highway_env.road.lane
 
 

 [docs]
-    def on_lane(self, position: np.ndarray, longitudinal: float = None, lateral: float = None, margin: float = 0) \
-            -> bool:
+    def on_lane(
+        self,
+        position: np.ndarray,
+        longitudinal: float = None,
+        lateral: float = None,
+        margin: float = 0,
+    ) -> bool:
         """
         Whether a given world position is on the lane.
 
@@ -405,8 +410,10 @@ 
Source code for highway_env.road.lane
         """
         if longitudinal is None or lateral is None:
             longitudinal, lateral = self.local_coordinates(position)
-        is_on = np.abs(lateral) <= self.width_at(longitudinal) / 2 + margin and \
-            -self.VEHICLE_LENGTH <= longitudinal < self.length + self.VEHICLE_LENGTH
+        is_on = (
+            np.abs(lateral) <= self.width_at(longitudinal) / 2 + margin
+            and -self.VEHICLE_LENGTH <= longitudinal < self.length + self.VEHICLE_LENGTH
+        )
         return is_on

 
 
@@ -422,12 +429,16 @@ 
Source code for highway_env.road.lane
         if self.forbidden:
             return False
         longitudinal, lateral = self.local_coordinates(position)
-        is_close = np.abs(lateral) <= 2 * self.width_at(longitudinal) and \
-            0 <= longitudinal < self.length + self.VEHICLE_LENGTH
+        is_close = (
+            np.abs(lateral) <= 2 * self.width_at(longitudinal)
+            and 0 <= longitudinal < self.length + self.VEHICLE_LENGTH
+        )
         return is_close

 
 
-    def after_end(self, position: np.ndarray, longitudinal: float = None, lateral: float = None) -> bool:
+    def after_end(
+        self, position: np.ndarray, longitudinal: float = None, lateral: float = None
+    ) -> bool:
         if not longitudinal:
             longitudinal, _ = self.local_coordinates(position)
         return longitudinal > self.length - self.VEHICLE_LENGTH / 2
@@ -442,13 +453,18 @@ 
Source code for highway_env.road.lane
 
 

 [docs]
-    def distance_with_heading(self, position: np.ndarray, heading: Optional[float], heading_weight: float = 1.0):
+    def distance_with_heading(
+        self,
+        position: np.ndarray,
+        heading: Optional[float],
+        heading_weight: float = 1.0,
+    ):
         """Compute a weighted distance in position and heading to the lane."""
         if heading is None:
             return self.distance(position)
         s, r = self.local_coordinates(position)
         angle = np.abs(self.local_angle(heading, s))
-        return abs(r) + max(s - self.length, 0) + max(0 - s, 0) + heading_weight*angle

+        return abs(r) + max(s - self.length, 0) + max(0 - s, 0) + heading_weight * angle

 
 
 

@@ -479,14 +495,16 @@ 
Source code for highway_env.road.lane
 
     """A lane going in straight line."""
 
-    def __init__(self,
-                 start: Vector,
-                 end: Vector,
-                 width: float = AbstractLane.DEFAULT_WIDTH,
-                 line_types: Tuple[LineType, LineType] = None,
-                 forbidden: bool = False,
-                 speed_limit: float = 20,
-                 priority: int = 0) -> None:
+    def __init__(
+        self,
+        start: Vector,
+        end: Vector,
+        width: float = AbstractLane.DEFAULT_WIDTH,
+        line_types: Tuple[LineType, LineType] = None,
+        forbidden: bool = False,
+        speed_limit: float = 20,
+        priority: int = 0,
+    ) -> None:
         """
         New straight lane.
 
@@ -500,7 +518,9 @@ 
Source code for highway_env.road.lane
         self.start = np.array(start)
         self.end = np.array(end)
         self.width = width
-        self.heading = np.arctan2(self.end[1] - self.start[1], self.end[0] - self.start[0])
+        self.heading = np.arctan2(
+            self.end[1] - self.start[1], self.end[0] - self.start[0]
+        )
         self.length = np.linalg.norm(self.end - self.start)
         self.line_types = line_types or [LineType.STRIPED, LineType.STRIPED]
         self.direction = (self.end - self.start) / self.length
@@ -512,7 +532,11 @@ 
Source code for highway_env.road.lane
 

 [docs]
     def position(self, longitudinal: float, lateral: float) -> np.ndarray:
-        return self.start + longitudinal * self.direction + lateral * self.direction_lateral

+        return (
+            self.start
+            + longitudinal * self.direction
+            + lateral * self.direction_lateral
+        )

 
 
 

@@ -557,8 +581,8 @@ 
Source code for highway_env.road.lane
                 "line_types": self.line_types,
                 "forbidden": self.forbidden,
                 "speed_limit": self.speed_limit,
-                "priority": self.priority
-            }
+                "priority": self.priority,
+            },
         }

 

 
@@ -570,17 +594,19 @@ 
Source code for highway_env.road.lane
 
     """A sinusoidal lane."""
 
-    def __init__(self,
-                 start: Vector,
-                 end: Vector,
-                 amplitude: float,
-                 pulsation: float,
-                 phase: float,
-                 width: float = StraightLane.DEFAULT_WIDTH,
-                 line_types: List[LineType] = None,
-                 forbidden: bool = False,
-                 speed_limit: float = 20,
-                 priority: int = 0) -> None:
+    def __init__(
+        self,
+        start: Vector,
+        end: Vector,
+        amplitude: float,
+        pulsation: float,
+        phase: float,
+        width: float = StraightLane.DEFAULT_WIDTH,
+        line_types: List[LineType] = None,
+        forbidden: bool = False,
+        speed_limit: float = 20,
+        priority: int = 0,
+    ) -> None:
         """
         New sinusoidal lane.
 
@@ -590,7 +616,9 @@ 
Source code for highway_env.road.lane
         :param pulsation: the lane pulsation [rad/m]
         :param phase: the lane initial phase [rad]
         """
-        super().__init__(start, end,  width, line_types, forbidden, speed_limit, priority)
+        super().__init__(
+            start, end, width, line_types, forbidden, speed_limit, priority
+        )
         self.amplitude = amplitude
         self.pulsation = pulsation
         self.phase = phase
@@ -598,22 +626,30 @@ 
Source code for highway_env.road.lane
 

 [docs]
     def position(self, longitudinal: float, lateral: float) -> np.ndarray:
-        return super().position(longitudinal,
-                                lateral + self.amplitude * np.sin(self.pulsation * longitudinal + self.phase))

+        return super().position(
+            longitudinal,
+            lateral
+            + self.amplitude * np.sin(self.pulsation * longitudinal + self.phase),
+        )

 
 
 

 [docs]
     def heading_at(self, longitudinal: float) -> float:
         return super().heading_at(longitudinal) + np.arctan(
-            self.amplitude * self.pulsation * np.cos(self.pulsation * longitudinal + self.phase))

+            self.amplitude
+            * self.pulsation
+            * np.cos(self.pulsation * longitudinal + self.phase)
+        )

 
 
 

 [docs]
     def local_coordinates(self, position: np.ndarray) -> Tuple[float, float]:
         longitudinal, lateral = super().local_coordinates(position)
-        return longitudinal, lateral - self.amplitude * np.sin(self.pulsation * longitudinal + self.phase)

+        return longitudinal, lateral - self.amplitude * np.sin(
+            self.pulsation * longitudinal + self.phase
+        )

 
 
 

@@ -629,14 +665,18 @@ 
Source code for highway_env.road.lane
 [docs]
     def to_config(self) -> dict:
         config = super().to_config()
-        config.update({
-            "class_path": get_class_path(self.__class__),
-        })
-        config["config"].update({
-            "amplitude": self.amplitude,
-            "pulsation": self.pulsation,
-            "phase": self.phase
-        })
+        config.update(
+            {
+                "class_path": get_class_path(self.__class__),
+            }
+        )
+        config["config"].update(
+            {
+                "amplitude": self.amplitude,
+                "pulsation": self.pulsation,
+                "phase": self.phase,
+            }
+        )
         return config

 

 
@@ -648,17 +688,19 @@ 
Source code for highway_env.road.lane
 
     """A lane going in circle arc."""
 
-    def __init__(self,
-                 center: Vector,
-                 radius: float,
-                 start_phase: float,
-                 end_phase: float,
-                 clockwise: bool = True,
-                 width: float = AbstractLane.DEFAULT_WIDTH,
-                 line_types: List[LineType] = None,
-                 forbidden: bool = False,
-                 speed_limit: float = 20,
-                 priority: int = 0) -> None:
+    def __init__(
+        self,
+        center: Vector,
+        radius: float,
+        start_phase: float,
+        end_phase: float,
+        clockwise: bool = True,
+        width: float = AbstractLane.DEFAULT_WIDTH,
+        line_types: List[LineType] = None,
+        forbidden: bool = False,
+        speed_limit: float = 20,
+        priority: int = 0,
+    ) -> None:
         super().__init__()
         self.center = np.array(center)
         self.radius = radius
@@ -669,7 +711,7 @@ 
Source code for highway_env.road.lane
         self.width = width
         self.line_types = line_types or [LineType.STRIPED, LineType.STRIPED]
         self.forbidden = forbidden
-        self.length = radius*(end_phase - start_phase) * self.direction
+        self.length = radius * (end_phase - start_phase) * self.direction
         self.priority = priority
         self.speed_limit = speed_limit
 
@@ -677,14 +719,16 @@ 
Source code for highway_env.road.lane
 [docs]
     def position(self, longitudinal: float, lateral: float) -> np.ndarray:
         phi = self.direction * longitudinal / self.radius + self.start_phase
-        return self.center + (self.radius - lateral * self.direction)*np.array([np.cos(phi), np.sin(phi)])

+        return self.center + (self.radius - lateral * self.direction) * np.array(
+            [np.cos(phi), np.sin(phi)]
+        )

 
 
 

 [docs]
     def heading_at(self, longitudinal: float) -> float:
         phi = self.direction * longitudinal / self.radius + self.start_phase
-        psi = phi + np.pi/2 * self.direction
+        psi = phi + np.pi / 2 * self.direction
         return psi

 
 
@@ -701,8 +745,8 @@ 
Source code for highway_env.road.lane
         phi = np.arctan2(delta[1], delta[0])
         phi = self.start_phase + utils.wrap_to_pi(phi - self.start_phase)
         r = np.linalg.norm(delta)
-        longitudinal = self.direction*(phi - self.start_phase)*self.radius
-        lateral = self.direction*(self.radius - r)
+        longitudinal = self.direction * (phi - self.start_phase) * self.radius
+        lateral = self.direction * (self.radius - r)
         return longitudinal, lateral

 
 
@@ -729,8 +773,8 @@ 
Source code for highway_env.road.lane
                 "line_types": self.line_types,
                 "forbidden": self.forbidden,
                 "speed_limit": self.speed_limit,
-                "priority": self.priority
-            }
+                "priority": self.priority,
+            },
         }

 

 
diff --git a/main/_modules/highway_env/road/regulation/index.html b/main/_modules/highway_env/road/regulation/index.html
index 57b24d8a7..f890dc602 100644
--- a/main/_modules/highway_env/road/regulation/index.html
+++ b/main/_modules/highway_env/road/regulation/index.html
@@ -298,7 +298,8 @@ 
Source code for highway_env.road.regulation
 from highway_env import utils
 from highway_env.road.road import Road, RoadNetwork
 from highway_env.vehicle.controller import ControlledVehicle, MDPVehicle
-from highway_env.vehicle.kinematics import Vehicle, Obstacle
+from highway_env.vehicle.kinematics import Vehicle
+from highway_env.vehicle.objects import Obstacle
 
 
 

@@ -306,10 +307,16 @@ 
Source code for highway_env.road.regulation
 class RegulatedRoad(Road):
     YIELDING_COLOR: Tuple[float, float, float] = None
     REGULATION_FREQUENCY: int = 2
-    YIELD_DURATION: float = 0.
-
-    def __init__(self, network: RoadNetwork = None, vehicles: List[Vehicle] = None, obstacles: List[Obstacle] = None,
-                 np_random: np.random.RandomState = None, record_history: bool = False) -> None:
+    YIELD_DURATION: float = 0.0
+
+    def __init__(
+        self,
+        network: RoadNetwork = None,
+        vehicles: List[Vehicle] = None,
+        obstacles: List[Obstacle] = None,
+        np_random: np.random.RandomState = None,
+        record_history: bool = False,
+    ) -> None:
         super().__init__(network, vehicles, obstacles, np_random, record_history)
         self.steps = 0
 
@@ -339,12 +346,16 @@ 
Source code for highway_env.road.regulation
 
         # Find new conflicts and resolve them
         for i in range(len(self.vehicles) - 1):
-            for j in range(i+1, len(self.vehicles)):
+            for j in range(i + 1, len(self.vehicles)):
                 if self.is_conflict_possible(self.vehicles[i], self.vehicles[j]):
-                    yielding_vehicle = self.respect_priorities(self.vehicles[i], self.vehicles[j])
-                    if yielding_vehicle is not None and \
-                            isinstance(yielding_vehicle, ControlledVehicle) and \
-                            not isinstance(yielding_vehicle, MDPVehicle):
+                    yielding_vehicle = self.respect_priorities(
+                        self.vehicles[i], self.vehicles[j]
+                    )
+                    if (
+                        yielding_vehicle is not None
+                        and isinstance(yielding_vehicle, ControlledVehicle)
+                        and not isinstance(yielding_vehicle, MDPVehicle)
+                    ):
                         yielding_vehicle.color = self.YIELDING_COLOR
                         yielding_vehicle.target_speed = 0
                         yielding_vehicle.is_yielding = True
@@ -371,19 +382,28 @@ 
Source code for highway_env.road.regulation
 
 
     @staticmethod
-    def is_conflict_possible(v1: ControlledVehicle, v2: ControlledVehicle, horizon: int = 3, step: float = 0.25) -> bool:
+    def is_conflict_possible(
+        v1: ControlledVehicle,
+        v2: ControlledVehicle,
+        horizon: int = 3,
+        step: float = 0.25,
+    ) -> bool:
         times = np.arange(step, horizon, step)
         positions_1, headings_1 = v1.predict_trajectory_constant_speed(times)
         positions_2, headings_2 = v2.predict_trajectory_constant_speed(times)
 
-        for position_1, heading_1, position_2, heading_2 in zip(positions_1, headings_1, positions_2, headings_2):
+        for position_1, heading_1, position_2, heading_2 in zip(
+            positions_1, headings_1, positions_2, headings_2
+        ):
             # Fast spherical pre-check
             if np.linalg.norm(position_2 - position_1) > v1.LENGTH:
                 continue
 
             # Accurate rectangular check
-            if utils.rotated_rectangles_intersect((position_1, 1.5*v1.LENGTH, 0.9*v1.WIDTH, heading_1),
-                                                  (position_2, 1.5*v2.LENGTH, 0.9*v2.WIDTH, heading_2)):
+            if utils.rotated_rectangles_intersect(
+                (position_1, 1.5 * v1.LENGTH, 0.9 * v1.WIDTH, heading_1),
+                (position_2, 1.5 * v2.LENGTH, 0.9 * v2.WIDTH, heading_2),
+            ):
                 return True

 
 

diff --git a/main/_modules/highway_env/road/road/index.html b/main/_modules/highway_env/road/road/index.html
index aaf71e350..fb6ef1cf8 100644
--- a/main/_modules/highway_env/road/road/index.html
+++ b/main/_modules/highway_env/road/road/index.html
@@ -291,11 +291,12 @@
           

             
             
Source code for highway_env.road.road
-import numpy as np
-import logging
-from typing import List, Tuple, Dict, TYPE_CHECKING, Optional
+import logging
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
 
-from highway_env.road.lane import LineType, StraightLane, AbstractLane, lane_from_config
+import numpy as np
+
+from highway_env.road.lane import AbstractLane, LineType, StraightLane, lane_from_config
 from highway_env.vehicle.objects import Landmark
 
 if TYPE_CHECKING:
@@ -341,7 +342,9 @@ 
Source code for highway_env.road.road
             _id = 0
         return self.graph[_from][_to][_id]
 
-    def get_closest_lane_index(self, position: np.ndarray, heading: Optional[float] = None) -> LaneIndex:
+    def get_closest_lane_index(
+        self, position: np.ndarray, heading: Optional[float] = None
+    ) -> LaneIndex:
         """
         Get the index of the lane closest to a world position.
 
@@ -357,8 +360,13 @@ 
Source code for highway_env.road.road
                     indexes.append((_from, _to, _id))
         return indexes[int(np.argmin(distances))]
 
-    def next_lane(self, current_index: LaneIndex, route: Route = None, position: np.ndarray = None,
-                  np_random: np.random.RandomState = np.random) -> LaneIndex:
+    def next_lane(
+        self,
+        current_index: LaneIndex,
+        route: Route = None,
+        position: np.ndarray = None,
+        np_random: np.random.RandomState = np.random,
+    ) -> LaneIndex:
         """
         Get the index of the next lane that should be followed after finishing the current lane.
 
@@ -376,12 +384,20 @@ 
Source code for highway_env.road.road
         next_to = next_id = None
         # Pick next road according to planned route
         if route:
-            if route[0][:2] == current_index[:2]:  # We just finished the first step of the route, drop it.
+            if (
+                route[0][:2] == current_index[:2]
+            ):  # We just finished the first step of the route, drop it.
                 route.pop(0)
-            if route and route[0][0] == _to:  # Next road in route is starting at the end of current road.
+            if (
+                route and route[0][0] == _to
+            ):  # Next road in route is starting at the end of current road.
                 _, next_to, next_id = route[0]
             elif route:
-                logger.warning("Route {} does not start after current road {}.".format(route[0], current_index))
+                logger.warning(
+                    "Route {} does not start after current road {}.".format(
+                        route[0], current_index
+                    )
+                )
 
         # Compute current projected (desired) position
         long, lat = self.get_lane(current_index).local_coordinates(position)
@@ -390,19 +406,34 @@ 
Source code for highway_env.road.road
         if not next_to:
             # Pick the one with the closest lane to projected target position
             try:
-                lanes_dists = [(next_to,
-                                *self.next_lane_given_next_road(_from, _to, _id, next_to, next_id, projected_position))
-                               for next_to in self.graph[_to].keys()]  # (next_to, next_id, distance)
+                lanes_dists = [
+                    (
+                        next_to,
+                        *self.next_lane_given_next_road(
+                            _from, _to, _id, next_to, next_id, projected_position
+                        ),
+                    )
+                    for next_to in self.graph[_to].keys()
+                ]  # (next_to, next_id, distance)
                 next_to, next_id, _ = min(lanes_dists, key=lambda x: x[-1])
             except KeyError:
                 return current_index
         else:
             # If it is known, follow it and get the closest lane
-            next_id, _ = self.next_lane_given_next_road(_from, _to, _id, next_to, next_id, projected_position)
+            next_id, _ = self.next_lane_given_next_road(
+                _from, _to, _id, next_to, next_id, projected_position
+            )
         return _to, next_to, next_id
 
-    def next_lane_given_next_road(self, _from: str, _to: str, _id: int,
-                                  next_to: str, next_id: int, position: np.ndarray) -> Tuple[int, float]:
+    def next_lane_given_next_road(
+        self,
+        _from: str,
+        _to: str,
+        _id: int,
+        next_to: str,
+        next_id: int,
+        position: np.ndarray,
+    ) -> Tuple[int, float]:
         # If next road has same number of lane, stay on the same lane
         if len(self.graph[_from][_to]) == len(self.graph[_to][next_to]):
             if next_id is None:
@@ -410,8 +441,9 @@ 
Source code for highway_env.road.road
         # Else, pick closest lane
         else:
             lanes = range(len(self.graph[_to][next_to]))
-            next_id = min(lanes,
-                          key=lambda l: self.get_lane((_to, next_to, l)).distance(position))
+            next_id = min(
+                lanes, key=lambda l: self.get_lane((_to, next_to, l)).distance(position)
+            )
         return next_id, self.get_lane((_to, next_to, next_id)).distance(position)
 
     def bfs_paths(self, start: str, goal: str) -> List[List[str]]:
@@ -427,7 +459,9 @@ 
Source code for highway_env.road.road
             (node, path) = queue.pop(0)
             if node not in self.graph:
                 yield []
-            for _next in sorted([key for key in self.graph[node].keys() if key not in path]):
+            for _next in sorted(
+                [key for key in self.graph[node].keys() if key not in path]
+            ):
                 if _next == goal:
                     yield path + [_next]
                 elif _next in self.graph:
@@ -448,13 +482,16 @@ 
Source code for highway_env.road.road
         :param lane_index: the index of a lane.
         :return: all lanes belonging to the same road.
         """
-        return [(lane_index[0], lane_index[1], i) for i in range(len(self.graph[lane_index[0]][lane_index[1]]))]
+        return [
+            (lane_index[0], lane_index[1], i)
+            for i in range(len(self.graph[lane_index[0]][lane_index[1]]))
+        ]
 
     def side_lanes(self, lane_index: LaneIndex) -> List[LaneIndex]:
         """
-                :param lane_index: the index of a lane.
-                :return: indexes of lanes next to a an input lane, to its right or left.
-                """
+        :param lane_index: the index of a lane.
+        :return: indexes of lanes next to a an input lane, to its right or left.
+        """
         _from, _to, _id = lane_index
         lanes = []
         if _id > 0:
@@ -464,17 +501,31 @@ 
Source code for highway_env.road.road
         return lanes
 
     @staticmethod
-    def is_same_road(lane_index_1: LaneIndex, lane_index_2: LaneIndex, same_lane: bool = False) -> bool:
+    def is_same_road(
+        lane_index_1: LaneIndex, lane_index_2: LaneIndex, same_lane: bool = False
+    ) -> bool:
         """Is lane 1 in the same road as lane 2?"""
-        return lane_index_1[:2] == lane_index_2[:2] and (not same_lane or lane_index_1[2] == lane_index_2[2])
+        return lane_index_1[:2] == lane_index_2[:2] and (
+            not same_lane or lane_index_1[2] == lane_index_2[2]
+        )
 
     @staticmethod
-    def is_leading_to_road(lane_index_1: LaneIndex, lane_index_2: LaneIndex, same_lane: bool = False) -> bool:
+    def is_leading_to_road(
+        lane_index_1: LaneIndex, lane_index_2: LaneIndex, same_lane: bool = False
+    ) -> bool:
         """Is lane 1 leading to of lane 2?"""
-        return lane_index_1[1] == lane_index_2[0] and (not same_lane or lane_index_1[2] == lane_index_2[2])
-
-    def is_connected_road(self, lane_index_1: LaneIndex, lane_index_2: LaneIndex, route: Route = None,
-                          same_lane: bool = False, depth: int = 0) -> bool:
+        return lane_index_1[1] == lane_index_2[0] and (
+            not same_lane or lane_index_1[2] == lane_index_2[2]
+        )
+
+    def is_connected_road(
+        self,
+        lane_index_1: LaneIndex,
+        lane_index_2: LaneIndex,
+        route: Route = None,
+        same_lane: bool = False,
+        depth: int = 0,
+    ) -> bool:
         """
         Is the lane 2 leading to a road within lane 1's route?
 
@@ -486,55 +537,86 @@ 
Source code for highway_env.road.road
         :param depth: search depth from lane 1 along its route
         :return: whether the roads are connected
         """
-        if RoadNetwork.is_same_road(lane_index_2, lane_index_1, same_lane) \
-                or RoadNetwork.is_leading_to_road(lane_index_2, lane_index_1, same_lane):
+        if RoadNetwork.is_same_road(
+            lane_index_2, lane_index_1, same_lane
+        ) or RoadNetwork.is_leading_to_road(lane_index_2, lane_index_1, same_lane):
             return True
         if depth > 0:
             if route and route[0][:2] == lane_index_1[:2]:
                 # Route is starting at current road, skip it
-                return self.is_connected_road(lane_index_1, lane_index_2, route[1:], same_lane, depth)
+                return self.is_connected_road(
+                    lane_index_1, lane_index_2, route[1:], same_lane, depth
+                )
             elif route and route[0][0] == lane_index_1[1]:
                 # Route is continuing from current road, follow it
-                return self.is_connected_road(route[0], lane_index_2, route[1:], same_lane, depth - 1)
+                return self.is_connected_road(
+                    route[0], lane_index_2, route[1:], same_lane, depth - 1
+                )
             else:
                 # Recursively search all roads at intersection
                 _from, _to, _id = lane_index_1
-                return any([self.is_connected_road((_to, l1_to, _id), lane_index_2, route, same_lane, depth - 1)
-                            for l1_to in self.graph.get(_to, {}).keys()])
+                return any(
+                    [
+                        self.is_connected_road(
+                            (_to, l1_to, _id), lane_index_2, route, same_lane, depth - 1
+                        )
+                        for l1_to in self.graph.get(_to, {}).keys()
+                    ]
+                )
         return False
 
     def lanes_list(self) -> List[AbstractLane]:
-        return [lane for to in self.graph.values() for ids in to.values() for lane in ids]
+        return [
+            lane for to in self.graph.values() for ids in to.values() for lane in ids
+        ]
 
     def lanes_dict(self) -> Dict[str, AbstractLane]:
-        return {(from_, to_, i): lane
-                for from_, tos in self.graph.items() for to_, ids in tos.items() for i, lane in enumerate(ids)}
+        return {
+            (from_, to_, i): lane
+            for from_, tos in self.graph.items()
+            for to_, ids in tos.items()
+            for i, lane in enumerate(ids)
+        }
 
     @staticmethod
-    def straight_road_network(lanes: int = 4,
-                              start: float = 0,
-                              length: float = 10000,
-                              angle: float = 0,
-                              speed_limit: float = 30,
-                              nodes_str: Optional[Tuple[str, str]] = None,
-                              net: Optional['RoadNetwork'] = None) \
-            -> 'RoadNetwork':
+    def straight_road_network(
+        lanes: int = 4,
+        start: float = 0,
+        length: float = 10000,
+        angle: float = 0,
+        speed_limit: float = 30,
+        nodes_str: Optional[Tuple[str, str]] = None,
+        net: Optional["RoadNetwork"] = None,
+    ) -> "RoadNetwork":
         net = net or RoadNetwork()
         nodes_str = nodes_str or ("0", "1")
         for lane in range(lanes):
             origin = np.array([start, lane * StraightLane.DEFAULT_WIDTH])
             end = np.array([start + length, lane * StraightLane.DEFAULT_WIDTH])
-            rotation = np.array([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]])
+            rotation = np.array(
+                [[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]]
+            )
             origin = rotation @ origin
             end = rotation @ end
-            line_types = [LineType.CONTINUOUS_LINE if lane == 0 else LineType.STRIPED,
-                          LineType.CONTINUOUS_LINE if lane == lanes - 1 else LineType.NONE]
-            net.add_lane(*nodes_str, StraightLane(origin, end, line_types=line_types, speed_limit=speed_limit))
+            line_types = [
+                LineType.CONTINUOUS_LINE if lane == 0 else LineType.STRIPED,
+                LineType.CONTINUOUS_LINE if lane == lanes - 1 else LineType.NONE,
+            ]
+            net.add_lane(
+                *nodes_str,
+                StraightLane(
+                    origin, end, line_types=line_types, speed_limit=speed_limit
+                )
+            )
         return net
 
-    def position_heading_along_route(self, route: Route, longitudinal: float, lateral: float,
-                                     current_lane_index: LaneIndex) \
-            -> Tuple[np.ndarray, float]:
+    def position_heading_along_route(
+        self,
+        route: Route,
+        longitudinal: float,
+        lateral: float,
+        current_lane_index: LaneIndex,
+    ) -> Tuple[np.ndarray, float]:
         """
         Get the absolute position and heading along a route composed of several lanes at some local coordinates.
 
@@ -544,13 +626,18 @@ 
Source code for highway_env.road.road
         :param current_lane_index: current lane index of the vehicle
         :return: position, heading
         """
+
         def _get_route_head_with_id(route_):
             lane_index_ = route_[0]
             if lane_index_[2] is None:
                 # We know which road segment will be followed by the vehicle, but not which lane.
                 # Hypothesis: the vehicle will keep the same lane_id as the current one.
-                id_ = (current_lane_index[2]
-                       if current_lane_index[2] < len(self.graph[current_lane_index[0]][current_lane_index[1]]) else 0)
+                id_ = (
+                    current_lane_index[2]
+                    if current_lane_index[2]
+                    < len(self.graph[current_lane_index[0]][current_lane_index[1]])
+                    else 0
+                )
                 lane_index_ = (lane_index_[0], lane_index_[1], id_)
             return lane_index_
 
@@ -560,8 +647,9 @@ 
Source code for highway_env.road.road
             route = route[1:]
             lane_index = _get_route_head_with_id(route)
 
-        return self.get_lane(lane_index).position(longitudinal, lateral),\
-               self.get_lane(lane_index).heading_at(longitudinal)
+        return self.get_lane(lane_index).position(longitudinal, lateral), self.get_lane(
+            lane_index
+        ).heading_at(longitudinal)
 
     def random_lane_index(self, np_random: np.random.RandomState) -> LaneIndex:
         _from = np_random.choice(list(self.graph.keys()))
@@ -577,9 +665,7 @@ 
Source code for highway_env.road.road
             for _to, lanes_dict in to_dict.items():
                 net.graph[_from][_to] = []
                 for lane_dict in lanes_dict:
-                    net.graph[_from][_to].append(
-                        lane_from_config(lane_dict)
-                    )
+                    net.graph[_from][_to].append(lane_from_config(lane_dict))
         return net
 
     def to_config(self) -> dict:
@@ -589,9 +675,7 @@ 
Source code for highway_env.road.road
             for _to, lanes in to_dict.items():
                 graph_dict[_from][_to] = []
                 for lane in lanes:
-                    graph_dict[_from][_to].append(
-                        lane.to_config()
-                    )
+                    graph_dict[_from][_to].append(lane.to_config())
         return graph_dict
 
 
@@ -601,12 +685,14 @@ 
Source code for highway_env.road.road
 
     """A road is a set of lanes, and a set of vehicles driving on these lanes."""
 
-    def __init__(self,
-                 network: RoadNetwork = None,
-                 vehicles: List['kinematics.Vehicle'] = None,
-                 road_objects: List['objects.RoadObject'] = None,
-                 np_random: np.random.RandomState = None,
-                 record_history: bool = False) -> None:
+    def __init__(
+        self,
+        network: RoadNetwork = None,
+        vehicles: List["kinematics.Vehicle"] = None,
+        road_objects: List["objects.RoadObject"] = None,
+        np_random: np.random.RandomState = None,
+        record_history: bool = False,
+    ) -> None:
         """
         New road.
 
@@ -622,15 +708,28 @@ 
Source code for highway_env.road.road
         self.np_random = np_random if np_random else np.random.RandomState()
         self.record_history = record_history
 
-    def close_objects_to(self, vehicle: 'kinematics.Vehicle', distance: float, count: Optional[int] = None,
-                         see_behind: bool = True, sort: bool = True, vehicles_only: bool = False) -> object:
-        vehicles = [v for v in self.vehicles
-                    if np.linalg.norm(v.position - vehicle.position) < distance
-                    and v is not vehicle
-                    and (see_behind or -2 * vehicle.LENGTH < vehicle.lane_distance_to(v))]
-        obstacles = [o for o in self.objects
-                     if np.linalg.norm(o.position - vehicle.position) < distance
-                     and -2 * vehicle.LENGTH < vehicle.lane_distance_to(o)]
+    def close_objects_to(
+        self,
+        vehicle: "kinematics.Vehicle",
+        distance: float,
+        count: Optional[int] = None,
+        see_behind: bool = True,
+        sort: bool = True,
+        vehicles_only: bool = False,
+    ) -> object:
+        vehicles = [
+            v
+            for v in self.vehicles
+            if np.linalg.norm(v.position - vehicle.position) < distance
+            and v is not vehicle
+            and (see_behind or -2 * vehicle.LENGTH < vehicle.lane_distance_to(v))
+        ]
+        obstacles = [
+            o
+            for o in self.objects
+            if np.linalg.norm(o.position - vehicle.position) < distance
+            and -2 * vehicle.LENGTH < vehicle.lane_distance_to(o)
+        ]
 
         objects_ = vehicles if vehicles_only else vehicles + obstacles
 
@@ -640,9 +739,17 @@ 
Source code for highway_env.road.road
             objects_ = objects_[:count]
         return objects_
 
-    def close_vehicles_to(self, vehicle: 'kinematics.Vehicle', distance: float, count: Optional[int] = None,
-                          see_behind: bool = True, sort: bool = True) -> object:
-        return self.close_objects_to(vehicle, distance, count, see_behind, sort, vehicles_only=True)
+    def close_vehicles_to(
+        self,
+        vehicle: "kinematics.Vehicle",
+        distance: float,
+        count: Optional[int] = None,
+        see_behind: bool = True,
+        sort: bool = True,
+    ) -> object:
+        return self.close_objects_to(
+            vehicle, distance, count, see_behind, sort, vehicles_only=True
+        )
 
 

 [docs]
@@ -663,7 +770,7 @@ 
Source code for highway_env.road.road
         for vehicle in self.vehicles:
             vehicle.step(dt)
         for i, vehicle in enumerate(self.vehicles):
-            for other in self.vehicles[i+1:]:
+            for other in self.vehicles[i + 1 :]:
                 vehicle.handle_collisions(other, dt)
             for other in self.objects:
                 vehicle.handle_collisions(other, dt)

@@ -671,8 +778,9 @@ 
Source code for highway_env.road.road
 
 

 [docs]
-    def neighbour_vehicles(self, vehicle: 'kinematics.Vehicle', lane_index: LaneIndex = None) \
-            -> Tuple[Optional['kinematics.Vehicle'], Optional['kinematics.Vehicle']]:
+    def neighbour_vehicles(
+        self, vehicle: "kinematics.Vehicle", lane_index: LaneIndex = None
+    ) -> Tuple[Optional["kinematics.Vehicle"], Optional["kinematics.Vehicle"]]:
         """
         Find the preceding and following vehicles of a given vehicle.
 
@@ -690,7 +798,9 @@ 
Source code for highway_env.road.road
         s_front = s_rear = None
         v_front = v_rear = None
         for v in self.vehicles + self.objects:
-            if v is not vehicle and not isinstance(v, Landmark):  # self.network.is_connected_road(v.lane_index,
+            if v is not vehicle and not isinstance(
+                v, Landmark
+            ):  # self.network.is_connected_road(v.lane_index,
                 # lane_index, same_lane=True):
                 s_v, lat_v = lane.local_coordinates(v.position)
                 if not lane.on_lane(v.position, s_v, lat_v, margin=1):
diff --git a/main/_modules/highway_env/vehicle/behavior/index.html b/main/_modules/highway_env/vehicle/behavior/index.html
index cf29cfd9b..636d77965 100644
--- a/main/_modules/highway_env/vehicle/behavior/index.html
+++ b/main/_modules/highway_env/vehicle/behavior/index.html
@@ -291,14 +291,14 @@
           

             
             
Source code for highway_env.vehicle.behavior
-from typing import Tuple, Union
+from typing import Union
 
 import numpy as np
 
-from highway_env.road.road import Road, Route, LaneIndex
+from highway_env import utils
+from highway_env.road.road import LaneIndex, Road, Route
 from highway_env.utils import Vector
 from highway_env.vehicle.controller import ControlledVehicle
-from highway_env import utils
 from highway_env.vehicle.kinematics import Vehicle
 
 
@@ -335,27 +335,33 @@ 
Source code for highway_env.vehicle.behavior
    """Range of delta when chosen randomly."""
 
     # Lateral policy parameters
-    POLITENESS = 0.  # in [0, 1]
+    POLITENESS = 0.0  # in [0, 1]
     LANE_CHANGE_MIN_ACC_GAIN = 0.2  # [m/s2]
     LANE_CHANGE_MAX_BRAKING_IMPOSED = 2.0  # [m/s2]
     LANE_CHANGE_DELAY = 1.0  # [s]
 
-    def __init__(self,
-                 road: Road,
-                 position: Vector,
-                 heading: float = 0,
-                 speed: float = 0,
-                 target_lane_index: int = None,
-                 target_speed: float = None,
-                 route: Route = None,
-                 enable_lane_change: bool = True,
-                 timer: float = None):
-        super().__init__(road, position, heading, speed, target_lane_index, target_speed, route)
+    def __init__(
+        self,
+        road: Road,
+        position: Vector,
+        heading: float = 0,
+        speed: float = 0,
+        target_lane_index: int = None,
+        target_speed: float = None,
+        route: Route = None,
+        enable_lane_change: bool = True,
+        timer: float = None,
+    ):
+        super().__init__(
+            road, position, heading, speed, target_lane_index, target_speed, route
+        )
         self.enable_lane_change = enable_lane_change
-        self.timer = timer or (np.sum(self.position)*np.pi) % self.LANE_CHANGE_DELAY
+        self.timer = timer or (np.sum(self.position) * np.pi) % self.LANE_CHANGE_DELAY
 
     def randomize_behavior(self):
-        self.DELTA = self.road.np_random.uniform(low=self.DELTA_RANGE[0], high=self.DELTA_RANGE[1])
+        self.DELTA = self.road.np_random.uniform(
+            low=self.DELTA_RANGE[0], high=self.DELTA_RANGE[1]
+        )
 
 

 [docs]
@@ -369,9 +375,16 @@ 
Source code for highway_env.vehicle.behavior
        :param vehicle: a vehicle
         :return: a new vehicle at the same dynamical state
         """
-        v = cls(vehicle.road, vehicle.position, heading=vehicle.heading, speed=vehicle.speed,
-                target_lane_index=vehicle.target_lane_index, target_speed=vehicle.target_speed,
-                route=vehicle.route, timer=getattr(vehicle, 'timer', None))
+        v = cls(
+            vehicle.road,
+            vehicle.position,
+            heading=vehicle.heading,
+            speed=vehicle.speed,
+            target_lane_index=vehicle.target_lane_index,
+            target_speed=vehicle.target_speed,
+            route=vehicle.route,
+            timer=getattr(vehicle, "timer", None),
+        )
         return v

 
 
@@ -393,24 +406,36 @@ 
Source code for highway_env.vehicle.behavior
self.follow_road()
         if self.enable_lane_change:
             self.change_lane_policy()
-        action['steering'] = self.steering_control(self.target_lane_index)
-        action['steering'] = np.clip(action['steering'], -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE)
+        action["steering"] = self.steering_control(self.target_lane_index)
+        action["steering"] = np.clip(
+            action["steering"], -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE
+        )
 
         # Longitudinal: IDM
-        front_vehicle, rear_vehicle = self.road.neighbour_vehicles(self, self.lane_index)
-        action['acceleration'] = self.acceleration(ego_vehicle=self,
-                                                   front_vehicle=front_vehicle,
-                                                   rear_vehicle=rear_vehicle)
+        front_vehicle, rear_vehicle = self.road.neighbour_vehicles(
+            self, self.lane_index
+        )
+        action["acceleration"] = self.acceleration(
+            ego_vehicle=self, front_vehicle=front_vehicle, rear_vehicle=rear_vehicle
+        )
         # When changing lane, check both current and target lanes
         if self.lane_index != self.target_lane_index:
-            front_vehicle, rear_vehicle = self.road.neighbour_vehicles(self, self.target_lane_index)
-            target_idm_acceleration = self.acceleration(ego_vehicle=self,
-                                                        front_vehicle=front_vehicle,
-                                                        rear_vehicle=rear_vehicle)
-            action['acceleration'] = min(action['acceleration'], target_idm_acceleration)
+            front_vehicle, rear_vehicle = self.road.neighbour_vehicles(
+                self, self.target_lane_index
+            )
+            target_idm_acceleration = self.acceleration(
+                ego_vehicle=self, front_vehicle=front_vehicle, rear_vehicle=rear_vehicle
+            )
+            action["acceleration"] = min(
+                action["acceleration"], target_idm_acceleration
+            )
         # action['acceleration'] = self.recover_from_stop(action['acceleration'])
-        action['acceleration'] = np.clip(action['acceleration'], -self.ACC_MAX, self.ACC_MAX)
-        Vehicle.act(self, action)  # Skip ControlledVehicle.act(), or the command will be overriden.

+        action["acceleration"] = np.clip(
+            action["acceleration"], -self.ACC_MAX, self.ACC_MAX
+        )
+        Vehicle.act(
+            self, action
+        )  # Skip ControlledVehicle.act(), or the command will be overriden.

 
 
 

@@ -429,10 +454,12 @@ 
Source code for highway_env.vehicle.behavior
 [docs]
-    def acceleration(self,
-                     ego_vehicle: ControlledVehicle,
-                     front_vehicle: Vehicle = None,
-                     rear_vehicle: Vehicle = None) -> float:
+    def acceleration(
+        self,
+        ego_vehicle: ControlledVehicle,
+        front_vehicle: Vehicle = None,
+        rear_vehicle: Vehicle = None,
+    ) -> float:
         """
         Compute an acceleration command with the Intelligent Driver Model.
 
@@ -451,20 +478,33 @@ 
Source code for highway_env.vehicle.behavior
return 0
         ego_target_speed = getattr(ego_vehicle, "target_speed", 0)
         if ego_vehicle.lane and ego_vehicle.lane.speed_limit is not None:
-            ego_target_speed = np.clip(ego_target_speed, 0, ego_vehicle.lane.speed_limit)
-        acceleration = self.COMFORT_ACC_MAX * (1 - np.power(
-            max(ego_vehicle.speed, 0) / abs(utils.not_zero(ego_target_speed)), self.DELTA))
+            ego_target_speed = np.clip(
+                ego_target_speed, 0, ego_vehicle.lane.speed_limit
+            )
+        acceleration = self.COMFORT_ACC_MAX * (
+            1
+            - np.power(
+                max(ego_vehicle.speed, 0) / abs(utils.not_zero(ego_target_speed)),
+                self.DELTA,
+            )
+        )
 
         if front_vehicle:
             d = ego_vehicle.lane_distance_to(front_vehicle)
-            acceleration -= self.COMFORT_ACC_MAX * \
-                np.power(self.desired_gap(ego_vehicle, front_vehicle) / utils.not_zero(d), 2)
+            acceleration -= self.COMFORT_ACC_MAX * np.power(
+                self.desired_gap(ego_vehicle, front_vehicle) / utils.not_zero(d), 2
+            )
         return acceleration

 
 
 

 [docs]
-    def desired_gap(self, ego_vehicle: Vehicle, front_vehicle: Vehicle = None, projected: bool = True) -> float:
+    def desired_gap(
+        self,
+        ego_vehicle: Vehicle,
+        front_vehicle: Vehicle = None,
+        projected: bool = True,
+    ) -> float:
         """
         Compute the desired distance between a vehicle and its leading vehicle.
 
@@ -476,9 +516,14 @@ 
Source code for highway_env.vehicle.behavior
d0 = self.DISTANCE_WANTED
         tau = self.TIME_WANTED
         ab = -self.COMFORT_ACC_MAX * self.COMFORT_ACC_MIN
-        dv = np.dot(ego_vehicle.velocity - front_vehicle.velocity, ego_vehicle.direction) if projected \
+        dv = (
+            np.dot(ego_vehicle.velocity - front_vehicle.velocity, ego_vehicle.direction)
+            if projected
             else ego_vehicle.speed - front_vehicle.speed
-        d_star = d0 + ego_vehicle.speed * tau + ego_vehicle.speed * dv / (2 * np.sqrt(ab))
+        )
+        d_star = (
+            d0 + ego_vehicle.speed * tau + ego_vehicle.speed * dv / (2 * np.sqrt(ab))
+        )
         return d_star

 
 
@@ -498,10 +543,12 @@ 
Source code for highway_env.vehicle.behavior
# If we are on correct route but bad lane: abort it if someone else is already changing into the same lane
             if self.lane_index[:2] == self.target_lane_index[:2]:
                 for v in self.road.vehicles:
-                    if v is not self \
-                            and v.lane_index != self.target_lane_index \
-                            and isinstance(v, ControlledVehicle) \
-                            and v.target_lane_index == self.target_lane_index:
+                    if (
+                        v is not self
+                        and v.lane_index != self.target_lane_index
+                        and isinstance(v, ControlledVehicle)
+                        and v.target_lane_index == self.target_lane_index
+                    ):
                         d = self.lane_distance_to(v)
                         d_star = self.desired_gap(self, v)
                         if 0 < d < d_star:
@@ -517,7 +564,9 @@ 
Source code for highway_env.vehicle.behavior
# decide to make a lane change
         for lane_index in self.road.network.side_lanes(self.lane_index):
             # Is the candidate lane close enough?
-            if not self.road.network.get_lane(lane_index).is_reachable_from(self.position):
+            if not self.road.network.get_lane(lane_index).is_reachable_from(
+                self.position
+            ):
                 continue
             # Only change lane when the vehicle is moving
             if np.abs(self.speed) < 1:
@@ -542,8 +591,12 @@ 
Source code for highway_env.vehicle.behavior
        """
         # Is the maneuver unsafe for the new following vehicle?
         new_preceding, new_following = self.road.neighbour_vehicles(self, lane_index)
-        new_following_a = self.acceleration(ego_vehicle=new_following, front_vehicle=new_preceding)
-        new_following_pred_a = self.acceleration(ego_vehicle=new_following, front_vehicle=self)
+        new_following_a = self.acceleration(
+            ego_vehicle=new_following, front_vehicle=new_preceding
+        )
+        new_following_pred_a = self.acceleration(
+            ego_vehicle=new_following, front_vehicle=self
+        )
         if new_following_pred_a < -self.LANE_CHANGE_MAX_BRAKING_IMPOSED:
             return False
 
@@ -552,7 +605,9 @@ 
Source code for highway_env.vehicle.behavior
self_pred_a = self.acceleration(ego_vehicle=self, front_vehicle=new_preceding)
         if self.route and self.route[0][2] is not None:
             # Wrong direction
-            if np.sign(lane_index[2] - self.target_lane_index[2]) != np.sign(self.route[0][2] - self.target_lane_index[2]):
+            if np.sign(lane_index[2] - self.target_lane_index[2]) != np.sign(
+                self.route[0][2] - self.target_lane_index[2]
+            ):
                 return False
             # Unsafe braking required
             elif self_pred_a < -self.LANE_CHANGE_MAX_BRAKING_IMPOSED:
@@ -561,10 +616,23 @@ 
Source code for highway_env.vehicle.behavior
# Is there an acceleration advantage for me and/or my followers to change lane?
         else:
             self_a = self.acceleration(ego_vehicle=self, front_vehicle=old_preceding)
-            old_following_a = self.acceleration(ego_vehicle=old_following, front_vehicle=self)
-            old_following_pred_a = self.acceleration(ego_vehicle=old_following, front_vehicle=old_preceding)
-            jerk = self_pred_a - self_a + self.POLITENESS * (new_following_pred_a - new_following_a
-                                                             + old_following_pred_a - old_following_a)
+            old_following_a = self.acceleration(
+                ego_vehicle=old_following, front_vehicle=self
+            )
+            old_following_pred_a = self.acceleration(
+                ego_vehicle=old_following, front_vehicle=old_preceding
+            )
+            jerk = (
+                self_pred_a
+                - self_a
+                + self.POLITENESS
+                * (
+                    new_following_pred_a
+                    - new_following_a
+                    + old_following_pred_a
+                    - old_following_a
+                )
+            )
             if jerk < self.LANE_CHANGE_MIN_ACC_GAIN:
                 return False
 
@@ -586,10 +654,13 @@ 
Source code for highway_env.vehicle.behavior
# Is the vehicle stopped on the wrong lane?
         if self.target_lane_index != self.lane_index and self.speed < stopped_speed:
             _, rear = self.road.neighbour_vehicles(self)
-            _, new_rear = self.road.neighbour_vehicles(self, self.road.network.get_lane(self.target_lane_index))
+            _, new_rear = self.road.neighbour_vehicles(
+                self, self.road.network.get_lane(self.target_lane_index)
+            )
             # Check for free room behind on both lanes
-            if (not rear or rear.lane_distance_to(self) > safe_distance) and \
-                    (not new_rear or new_rear.lane_distance_to(self) > safe_distance):
+            if (not rear or rear.lane_distance_to(self) > safe_distance) and (
+                not new_rear or new_rear.lane_distance_to(self) > safe_distance
+            ):
                 # Reverse
                 return -self.COMFORT_ACC_MAX / 2
         return acceleration

@@ -604,27 +675,50 @@ 
Source code for highway_env.vehicle.behavior
    """A Vehicle whose longitudinal and lateral controllers are linear with respect to parameters."""
 
     ACCELERATION_PARAMETERS = [0.3, 0.3, 2.0]
-    STEERING_PARAMETERS = [ControlledVehicle.KP_HEADING, ControlledVehicle.KP_HEADING * ControlledVehicle.KP_LATERAL]
-
-    ACCELERATION_RANGE = np.array([0.5*np.array(ACCELERATION_PARAMETERS), 1.5*np.array(ACCELERATION_PARAMETERS)])
-    STEERING_RANGE = np.array([np.array(STEERING_PARAMETERS) - np.array([0.07, 1.5]),
-                               np.array(STEERING_PARAMETERS) + np.array([0.07, 1.5])])
+    STEERING_PARAMETERS = [
+        ControlledVehicle.KP_HEADING,
+        ControlledVehicle.KP_HEADING * ControlledVehicle.KP_LATERAL,
+    ]
+
+    ACCELERATION_RANGE = np.array(
+        [
+            0.5 * np.array(ACCELERATION_PARAMETERS),
+            1.5 * np.array(ACCELERATION_PARAMETERS),
+        ]
+    )
+    STEERING_RANGE = np.array(
+        [
+            np.array(STEERING_PARAMETERS) - np.array([0.07, 1.5]),
+            np.array(STEERING_PARAMETERS) + np.array([0.07, 1.5]),
+        ]
+    )
 
     TIME_WANTED = 2.5
 
-    def __init__(self,
-                 road: Road,
-                 position: Vector,
-                 heading: float = 0,
-                 speed: float = 0,
-                 target_lane_index: int = None,
-                 target_speed: float = None,
-                 route: Route = None,
-                 enable_lane_change: bool = True,
-                 timer: float = None,
-                 data: dict = None):
-        super().__init__(road, position, heading, speed, target_lane_index, target_speed, route,
-                         enable_lane_change, timer)
+    def __init__(
+        self,
+        road: Road,
+        position: Vector,
+        heading: float = 0,
+        speed: float = 0,
+        target_lane_index: int = None,
+        target_speed: float = None,
+        route: Route = None,
+        enable_lane_change: bool = True,
+        timer: float = None,
+        data: dict = None,
+    ):
+        super().__init__(
+            road,
+            position,
+            heading,
+            speed,
+            target_lane_index,
+            target_speed,
+            route,
+            enable_lane_change,
+            timer,
+        )
         self.data = data if data is not None else {}
         self.collecting_data = True
 
@@ -638,17 +732,22 @@ 
Source code for highway_env.vehicle.behavior
def randomize_behavior(self):
         ua = self.road.np_random.uniform(size=np.shape(self.ACCELERATION_PARAMETERS))
-        self.ACCELERATION_PARAMETERS = self.ACCELERATION_RANGE[0] + ua*(self.ACCELERATION_RANGE[1] -
-                                                                        self.ACCELERATION_RANGE[0])
+        self.ACCELERATION_PARAMETERS = self.ACCELERATION_RANGE[0] + ua * (
+            self.ACCELERATION_RANGE[1] - self.ACCELERATION_RANGE[0]
+        )
         ub = self.road.np_random.uniform(size=np.shape(self.STEERING_PARAMETERS))
-        self.STEERING_PARAMETERS = self.STEERING_RANGE[0] + ub*(self.STEERING_RANGE[1] - self.STEERING_RANGE[0])
+        self.STEERING_PARAMETERS = self.STEERING_RANGE[0] + ub * (
+            self.STEERING_RANGE[1] - self.STEERING_RANGE[0]
+        )
 
 

 [docs]
-    def acceleration(self,
-                     ego_vehicle: ControlledVehicle,
-                     front_vehicle: Vehicle = None,
-                     rear_vehicle: Vehicle = None) -> float:
+    def acceleration(
+        self,
+        ego_vehicle: ControlledVehicle,
+        front_vehicle: Vehicle = None,
+        rear_vehicle: Vehicle = None,
+    ) -> float:
         """
         Compute an acceleration command with a Linear Model.
 
@@ -664,17 +763,27 @@ 
Source code for highway_env.vehicle.behavior
        :param rear_vehicle: the vehicle following the ego-vehicle
         :return: the acceleration command for the ego-vehicle [m/s2]
         """
-        return float(np.dot(self.ACCELERATION_PARAMETERS,
-                            self.acceleration_features(ego_vehicle, front_vehicle, rear_vehicle)))

-
-
-    def acceleration_features(self, ego_vehicle: ControlledVehicle,
-                              front_vehicle: Vehicle = None,
-                              rear_vehicle: Vehicle = None) -> np.ndarray:
+        return float(
+            np.dot(
+                self.ACCELERATION_PARAMETERS,
+                self.acceleration_features(ego_vehicle, front_vehicle, rear_vehicle),
+            )
+        )

+
+
+    def acceleration_features(
+        self,
+        ego_vehicle: ControlledVehicle,
+        front_vehicle: Vehicle = None,
+        rear_vehicle: Vehicle = None,
+    ) -> np.ndarray:
         vt, dv, dp = 0, 0, 0
         if ego_vehicle:
             vt = ego_vehicle.target_speed - ego_vehicle.speed
-            d_safe = self.DISTANCE_WANTED + np.maximum(ego_vehicle.speed, 0) * self.TIME_WANTED
+            d_safe = (
+                self.DISTANCE_WANTED
+                + np.maximum(ego_vehicle.speed, 0) * self.TIME_WANTED
+            )
             if front_vehicle:
                 d = ego_vehicle.lane_distance_to(front_vehicle)
                 dv = min(front_vehicle.speed - ego_vehicle.speed, 0)
@@ -692,7 +801,12 @@ 
Source code for highway_env.vehicle.behavior
        :param target_lane_index: index of the lane to follow
         :return: a steering wheel angle command [rad]
         """
-        return float(np.dot(np.array(self.STEERING_PARAMETERS), self.steering_features(target_lane_index)))

+        return float(
+            np.dot(
+                np.array(self.STEERING_PARAMETERS),
+                self.steering_features(target_lane_index),
+            )
+        )

 
 
 

@@ -708,41 +822,28 @@ 
Source code for highway_env.vehicle.behavior
lane_coords = lane.local_coordinates(self.position)
         lane_next_coords = lane_coords[0] + self.speed * self.TAU_PURSUIT
         lane_future_heading = lane.heading_at(lane_next_coords)
-        features = np.array([utils.wrap_to_pi(lane_future_heading - self.heading) *
-                             self.LENGTH / utils.not_zero(self.speed),
-                             -lane_coords[1] * self.LENGTH / (utils.not_zero(self.speed) ** 2)])
+        features = np.array(
+            [
+                utils.wrap_to_pi(lane_future_heading - self.heading)
+                * self.LENGTH
+                / utils.not_zero(self.speed),
+                -lane_coords[1] * self.LENGTH / (utils.not_zero(self.speed) ** 2),
+            ]
+        )
         return features

 
 
     def longitudinal_structure(self):
         # Nominal dynamics: integrate speed
-        A = np.array([
-            [0, 0, 1, 0],
-            [0, 0, 0, 1],
-            [0, 0, 0, 0],
-            [0, 0, 0, 0]
-        ])
+        A = np.array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0]])
         # Target speed dynamics
-        phi0 = np.array([
-            [0, 0, 0, 0],
-            [0, 0, 0, 0],
-            [0, 0, -1, 0],
-            [0, 0, 0, -1]
-        ])
+        phi0 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]])
         # Front speed control
-        phi1 = np.array([
-            [0, 0, 0, 0],
-            [0, 0, 0, 0],
-            [0, 0, -1, 1],
-            [0, 0, 0, 0]
-        ])
+        phi1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, -1, 1], [0, 0, 0, 0]])
         # Front position control
-        phi2 = np.array([
-            [0, 0, 0, 0],
-            [0, 0, 0, 0],
-            [-1, 1, -self.TIME_WANTED, 0],
-            [0, 0, 0, 0]
-        ])
+        phi2 = np.array(
+            [[0, 0, 0, 0], [0, 0, 0, 0], [-1, 1, -self.TIME_WANTED, 0], [0, 0, 0, 0]]
+        )
         # Disable speed control
         front_vehicle, _ = self.road.neighbour_vehicles(self)
         if not front_vehicle or self.speed < front_vehicle.speed:
@@ -760,18 +861,9 @@ 
Source code for highway_env.vehicle.behavior
return A, phi
 
     def lateral_structure(self):
-        A = np.array([
-            [0, 1],
-            [0, 0]
-        ])
-        phi0 = np.array([
-            [0, 0],
-            [0, -1]
-        ])
-        phi1 = np.array([
-            [0, 0],
-            [-1, 0]
-        ])
+        A = np.array([[0, 1], [0, 0]])
+        phi0 = np.array([[0, 0], [0, -1]])
+        phi1 = np.array([[0, 0], [-1, 0]])
         phi = np.array([phi0, phi1])
         return A, phi
 
@@ -783,7 +875,6 @@ 
Source code for highway_env.vehicle.behavior
def add_features(self, data, lane_index, output_lane=None):
-
         front_vehicle, rear_vehicle = self.road.neighbour_vehicles(self)
         features = self.acceleration_features(self, front_vehicle, rear_vehicle)
         output = np.dot(self.ACCELERATION_PARAMETERS, features)
@@ -811,9 +902,11 @@ 
Source code for highway_env.vehicle.behavior
MERGE_ACC_GAIN = 0.8
     MERGE_VEL_RATIO = 0.75
     MERGE_TARGET_VEL = 30
-    ACCELERATION_PARAMETERS = [MERGE_ACC_GAIN / ((1 - MERGE_VEL_RATIO) * MERGE_TARGET_VEL),
-                               MERGE_ACC_GAIN / (MERGE_VEL_RATIO * MERGE_TARGET_VEL),
-                               0.5]

+    ACCELERATION_PARAMETERS = [
+        MERGE_ACC_GAIN / ((1 - MERGE_VEL_RATIO) * MERGE_TARGET_VEL),
+        MERGE_ACC_GAIN / (MERGE_VEL_RATIO * MERGE_TARGET_VEL),
+        0.5,
+    ]

 
 
 
@@ -824,9 +917,11 @@ 
Source code for highway_env.vehicle.behavior
MERGE_ACC_GAIN = 1.2
     MERGE_VEL_RATIO = 0.75
     MERGE_TARGET_VEL = 30
-    ACCELERATION_PARAMETERS = [MERGE_ACC_GAIN / ((1 - MERGE_VEL_RATIO) * MERGE_TARGET_VEL),
-                               MERGE_ACC_GAIN / (MERGE_VEL_RATIO * MERGE_TARGET_VEL),
-                               2.0]

+    ACCELERATION_PARAMETERS = [
+        MERGE_ACC_GAIN / ((1 - MERGE_VEL_RATIO) * MERGE_TARGET_VEL),
+        MERGE_ACC_GAIN / (MERGE_VEL_RATIO * MERGE_TARGET_VEL),
+        2.0,
+    ]

 
 

           

diff --git a/main/_modules/highway_env/vehicle/controller/index.html b/main/_modules/highway_env/vehicle/controller/index.html
index 9b7ce1478..74bf52f0d 100644
--- a/main/_modules/highway_env/vehicle/controller/index.html
+++ b/main/_modules/highway_env/vehicle/controller/index.html
@@ -291,12 +291,13 @@
           

             
             
Source code for highway_env.vehicle.controller
-from typing import List, Tuple, Union, Optional
+import copy
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
-import copy
+
 from highway_env import utils
-from highway_env.road.road import Road, LaneIndex, Route
+from highway_env.road.road import LaneIndex, Road, Route
 from highway_env.utils import Vector
 from highway_env.vehicle.kinematics import Vehicle
 
@@ -326,14 +327,16 @@ 
Source code for highway_env.vehicle.controller
MAX_STEERING_ANGLE = np.pi / 3  # [rad]
     DELTA_SPEED = 5  # [m/s]
 
-    def __init__(self,
-                 road: Road,
-                 position: Vector,
-                 heading: float = 0,
-                 speed: float = 0,
-                 target_lane_index: LaneIndex = None,
-                 target_speed: float = None,
-                 route: Route = None):
+    def __init__(
+        self,
+        road: Road,
+        position: Vector,
+        heading: float = 0,
+        speed: float = 0,
+        target_lane_index: LaneIndex = None,
+        target_speed: float = None,
+        route: Route = None,
+    ):
         super().__init__(road, position, heading, speed)
         self.target_lane_index = target_lane_index or self.lane_index
         self.target_speed = target_speed or self.speed
@@ -351,9 +354,15 @@ 
Source code for highway_env.vehicle.controller
        :param vehicle: a vehicle
         :return: a new vehicle at the same dynamical state
         """
-        v = cls(vehicle.road, vehicle.position, heading=vehicle.heading, speed=vehicle.speed,
-                target_lane_index=vehicle.target_lane_index, target_speed=vehicle.target_speed,
-                route=vehicle.route)
+        v = cls(
+            vehicle.road,
+            vehicle.position,
+            heading=vehicle.heading,
+            speed=vehicle.speed,
+            target_lane_index=vehicle.target_lane_index,
+            target_speed=vehicle.target_speed,
+            route=vehicle.route,
+        )
         return v

 
 
@@ -370,7 +379,9 @@ 
Source code for highway_env.vehicle.controller
except KeyError:
             path = []
         if path:
-            self.route = [self.lane_index] + [(path[i], path[i + 1], None) for i in range(len(path) - 1)]
+            self.route = [self.lane_index] + [
+                (path[i], path[i + 1], None) for i in range(len(path) - 1)
+            ]
         else:
             self.route = [self.lane_index]
         return self

@@ -394,18 +405,34 @@ 
Source code for highway_env.vehicle.controller
self.target_speed -= self.DELTA_SPEED
         elif action == "LANE_RIGHT":
             _from, _to, _id = self.target_lane_index
-            target_lane_index = _from, _to, np.clip(_id + 1, 0, len(self.road.network.graph[_from][_to]) - 1)
-            if self.road.network.get_lane(target_lane_index).is_reachable_from(self.position):
+            target_lane_index = (
+                _from,
+                _to,
+                np.clip(_id + 1, 0, len(self.road.network.graph[_from][_to]) - 1),
+            )
+            if self.road.network.get_lane(target_lane_index).is_reachable_from(
+                self.position
+            ):
                 self.target_lane_index = target_lane_index
         elif action == "LANE_LEFT":
             _from, _to, _id = self.target_lane_index
-            target_lane_index = _from, _to, np.clip(_id - 1, 0, len(self.road.network.graph[_from][_to]) - 1)
-            if self.road.network.get_lane(target_lane_index).is_reachable_from(self.position):
+            target_lane_index = (
+                _from,
+                _to,
+                np.clip(_id - 1, 0, len(self.road.network.graph[_from][_to]) - 1),
+            )
+            if self.road.network.get_lane(target_lane_index).is_reachable_from(
+                self.position
+            ):
                 self.target_lane_index = target_lane_index
 
-        action = {"steering": self.steering_control(self.target_lane_index),
-                  "acceleration": self.speed_control(self.target_speed)}
-        action['steering'] = np.clip(action['steering'], -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE)
+        action = {
+            "steering": self.steering_control(self.target_lane_index),
+            "acceleration": self.speed_control(self.target_speed),
+        }
+        action["steering"] = np.clip(
+            action["steering"], -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE
+        )
         super().act(action)

 
 
@@ -414,10 +441,12 @@ 
Source code for highway_env.vehicle.controller
def follow_road(self) -> None:
         """At the end of a lane, automatically switch to a next one."""
         if self.road.network.get_lane(self.target_lane_index).after_end(self.position):
-            self.target_lane_index = self.road.network.next_lane(self.target_lane_index,
-                                                                 route=self.route,
-                                                                 position=self.position,
-                                                                 np_random=self.road.np_random)

+            self.target_lane_index = self.road.network.next_lane(
+                self.target_lane_index,
+                route=self.route,
+                position=self.position,
+                np_random=self.road.np_random,
+            )

 
 
 

@@ -440,16 +469,30 @@ 
Source code for highway_env.vehicle.controller
lane_future_heading = target_lane.heading_at(lane_next_coords)
 
         # Lateral position control
-        lateral_speed_command = - self.KP_LATERAL * lane_coords[1]
+        lateral_speed_command = -self.KP_LATERAL * lane_coords[1]
         # Lateral speed to heading
-        heading_command = np.arcsin(np.clip(lateral_speed_command / utils.not_zero(self.speed), -1, 1))
-        heading_ref = lane_future_heading + np.clip(heading_command, -np.pi/4, np.pi/4)
+        heading_command = np.arcsin(
+            np.clip(lateral_speed_command / utils.not_zero(self.speed), -1, 1)
+        )
+        heading_ref = lane_future_heading + np.clip(
+            heading_command, -np.pi / 4, np.pi / 4
+        )
         # Heading control
-        heading_rate_command = self.KP_HEADING * utils.wrap_to_pi(heading_ref - self.heading)
+        heading_rate_command = self.KP_HEADING * utils.wrap_to_pi(
+            heading_ref - self.heading
+        )
         # Heading rate to steering angle
-        slip_angle = np.arcsin(np.clip(self.LENGTH / 2 / utils.not_zero(self.speed) * heading_rate_command, -1, 1))
+        slip_angle = np.arcsin(
+            np.clip(
+                self.LENGTH / 2 / utils.not_zero(self.speed) * heading_rate_command,
+                -1,
+                1,
+            )
+        )
         steering_angle = np.arctan(2 * np.tan(slip_angle))
-        steering_angle = np.clip(steering_angle, -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE)
+        steering_angle = np.clip(
+            steering_angle, -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE
+        )
         return float(steering_angle)

 
 
@@ -483,8 +526,11 @@ 
Source code for highway_env.vehicle.controller
else:
             return [self.route]
         next_destinations_from = list(next_destinations.keys())
-        routes = [self.route[0:index+1] + [(self.route[index][1], destination, self.route[index][2])]
-                  for destination in next_destinations_from]
+        routes = [
+            self.route[0 : index + 1]
+            + [(self.route[index][1], destination, self.route[index][2])]
+            for destination in next_destinations_from
+        ]
         return routes

 
 
@@ -508,7 +554,9 @@ 
Source code for highway_env.vehicle.controller

 [docs]
-    def predict_trajectory_constant_speed(self, times: np.ndarray) -> Tuple[List[np.ndarray], List[float]]:
+    def predict_trajectory_constant_speed(
+        self, times: np.ndarray
+    ) -> Tuple[List[np.ndarray], List[float]]:
         """
         Predict the future positions of the vehicle along its planned route, under constant speed
 
@@ -517,11 +565,12 @@ 
Source code for highway_env.vehicle.controller
        """
         coordinates = self.lane.local_coordinates(self.position)
         route = self.route or [self.lane_index]
-        pos_heads = [self.road.network.position_heading_along_route(route,
-                                                                    coordinates[0] + self.speed * t,
-                                                                    0,
-                                                                    self.lane_index)
-                     for t in times]
+        pos_heads = [
+            self.road.network.position_heading_along_route(
+                route, coordinates[0] + self.speed * t, 0, self.lane_index
+            )
+            for t in times
+        ]
         return tuple(zip(*pos_heads))

 

 
@@ -532,17 +581,20 @@ 
Source code for highway_env.vehicle.controller
class MDPVehicle(ControlledVehicle):
 
     """A controlled vehicle with a specified discrete range of allowed target speeds."""
+
     DEFAULT_TARGET_SPEEDS = np.linspace(20, 30, 3)
 
-    def __init__(self,
-                 road: Road,
-                 position: List[float],
-                 heading: float = 0,
-                 speed: float = 0,
-                 target_lane_index: Optional[LaneIndex] = None,
-                 target_speed: Optional[float] = None,
-                 target_speeds: Optional[Vector] = None,
-                 route: Optional[Route] = None) -> None:
+    def __init__(
+        self,
+        road: Road,
+        position: List[float],
+        heading: float = 0,
+        speed: float = 0,
+        target_lane_index: Optional[LaneIndex] = None,
+        target_speed: Optional[float] = None,
+        target_speeds: Optional[Vector] = None,
+        route: Optional[Route] = None,
+    ) -> None:
         """
         Initializes an MDPVehicle
 
@@ -555,8 +607,14 @@ 
Source code for highway_env.vehicle.controller
        :param target_speeds: the discrete list of speeds the vehicle is able to track, through faster/slower actions
         :param route: the planned route of the vehicle, to handle intersections
         """
-        super().__init__(road, position, heading, speed, target_lane_index, target_speed, route)
-        self.target_speeds = np.array(target_speeds) if target_speeds is not None else self.DEFAULT_TARGET_SPEEDS
+        super().__init__(
+            road, position, heading, speed, target_lane_index, target_speed, route
+        )
+        self.target_speeds = (
+            np.array(target_speeds)
+            if target_speeds is not None
+            else self.DEFAULT_TARGET_SPEEDS
+        )
         self.speed_index = self.speed_to_index(self.target_speed)
         self.target_speed = self.index_to_speed(self.speed_index)
 
@@ -578,7 +636,9 @@ 
Source code for highway_env.vehicle.controller
else:
             super().act(action)
             return
-        self.speed_index = int(np.clip(self.speed_index, 0, self.target_speeds.size - 1))
+        self.speed_index = int(
+            np.clip(self.speed_index, 0, self.target_speeds.size - 1)
+        )
         self.target_speed = self.index_to_speed(self.speed_index)
         super().act()

 
@@ -606,8 +666,16 @@ 
Source code for highway_env.vehicle.controller
        :param speed: an input speed [m/s]
         :return: the index of the closest speed allowed []
         """
-        x = (speed - self.target_speeds[0]) / (self.target_speeds[-1] - self.target_speeds[0])
-        return np.int64(np.clip(np.round(x * (self.target_speeds.size - 1)), 0, self.target_speeds.size - 1))

+        x = (speed - self.target_speeds[0]) / (
+            self.target_speeds[-1] - self.target_speeds[0]
+        )
+        return np.int64(
+            np.clip(
+                np.round(x * (self.target_speeds.size - 1)),
+                0,
+                self.target_speeds.size - 1,
+            )
+        )

 
 
 

@@ -622,19 +690,33 @@ 
Source code for highway_env.vehicle.controller
        :param speed: an input speed [m/s]
         :return: the index of the closest speed allowed []
         """
-        x = (speed - cls.DEFAULT_TARGET_SPEEDS[0]) / (cls.DEFAULT_TARGET_SPEEDS[-1] - cls.DEFAULT_TARGET_SPEEDS[0])
-        return np.int64(np.clip(
-            np.round(x * (cls.DEFAULT_TARGET_SPEEDS.size - 1)), 0, cls.DEFAULT_TARGET_SPEEDS.size - 1))

+        x = (speed - cls.DEFAULT_TARGET_SPEEDS[0]) / (
+            cls.DEFAULT_TARGET_SPEEDS[-1] - cls.DEFAULT_TARGET_SPEEDS[0]
+        )
+        return np.int64(
+            np.clip(
+                np.round(x * (cls.DEFAULT_TARGET_SPEEDS.size - 1)),
+                0,
+                cls.DEFAULT_TARGET_SPEEDS.size - 1,
+            )
+        )

 
 
     @classmethod
     def get_speed_index(cls, vehicle: Vehicle) -> int:
-        return getattr(vehicle, "speed_index", cls.speed_to_index_default(vehicle.speed))
+        return getattr(
+            vehicle, "speed_index", cls.speed_to_index_default(vehicle.speed)
+        )
 
 

 [docs]
-    def predict_trajectory(self, actions: List, action_duration: float, trajectory_timestep: float, dt: float) \
-            -> List[ControlledVehicle]:
+    def predict_trajectory(
+        self,
+        actions: List,
+        action_duration: float,
+        trajectory_timestep: float,
+        dt: float,
+    ) -> List[ControlledVehicle]:
         """
         Predict the future trajectory of the vehicle given a sequence of actions.
 
diff --git a/main/_modules/highway_env/vehicle/kinematics/index.html b/main/_modules/highway_env/vehicle/kinematics/index.html
index 68fab820c..d08ca01d1 100644
--- a/main/_modules/highway_env/vehicle/kinematics/index.html
+++ b/main/_modules/highway_env/vehicle/kinematics/index.html
@@ -291,15 +291,15 @@
           

             
             
Source code for highway_env.vehicle.kinematics
-from typing import Union, Optional, Tuple, List
-import numpy as np
-import copy
+import copy
 from collections import deque
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
 
-from highway_env import utils
-from highway_env.road.road import Road, LaneIndex
-from highway_env.vehicle.objects import RoadObject, Obstacle, Landmark
+from highway_env.road.road import Road
 from highway_env.utils import Vector
+from highway_env.vehicle.objects import RoadObject
 
 
 

@@ -319,22 +319,24 @@ 
Source code for highway_env.vehicle.kinematics
    """ Vehicle width [m] """
     DEFAULT_INITIAL_SPEEDS = [23, 25]
     """ Range for random initial speeds [m/s] """
-    MAX_SPEED = 40.
+    MAX_SPEED = 40.0
     """ Maximum reachable speed [m/s] """
-    MIN_SPEED = -40.
+    MIN_SPEED = -40.0
     """ Minimum reachable speed [m/s] """
     HISTORY_SIZE = 30
     """ Length of the vehicle state history, for trajectory display"""
 
-    def __init__(self,
-                 road: Road,
-                 position: Vector,
-                 heading: float = 0,
-                 speed: float = 0,
-                 predition_type: str = 'constant_steering'):
+    def __init__(
+        self,
+        road: Road,
+        position: Vector,
+        heading: float = 0,
+        speed: float = 0,
+        predition_type: str = "constant_steering",
+    ):
         super().__init__(road, position, heading, speed)
         self.prediction_type = predition_type
-        self.action = {'steering': 0, 'acceleration': 0}
+        self.action = {"steering": 0, "acceleration": 0}
         self.crashed = False
         self.impact = None
         self.log = []
@@ -343,13 +345,15 @@ 
Source code for highway_env.vehicle.kinematics

 [docs]
     @classmethod
-    def create_random(cls, road: Road,
-                      speed: float = None,
-                      lane_from: Optional[str] = None,
-                      lane_to: Optional[str] = None,
-                      lane_id: Optional[int] = None,
-                      spacing: float = 1) \
-            -> "Vehicle":
+    def create_random(
+        cls,
+        road: Road,
+        speed: float = None,
+        lane_from: Optional[str] = None,
+        lane_to: Optional[str] = None,
+        lane_id: Optional[int] = None,
+        spacing: float = 1,
+    ) -> "Vehicle":
         """
         Create a random vehicle on the road.
 
@@ -366,17 +370,32 @@ 
Source code for highway_env.vehicle.kinematics
        """
         _from = lane_from or road.np_random.choice(list(road.network.graph.keys()))
         _to = lane_to or road.np_random.choice(list(road.network.graph[_from].keys()))
-        _id = lane_id if lane_id is not None else road.np_random.choice(len(road.network.graph[_from][_to]))
+        _id = (
+            lane_id
+            if lane_id is not None
+            else road.np_random.choice(len(road.network.graph[_from][_to]))
+        )
         lane = road.network.get_lane((_from, _to, _id))
         if speed is None:
             if lane.speed_limit is not None:
-                speed = road.np_random.uniform(0.7*lane.speed_limit, 0.8*lane.speed_limit)
+                speed = road.np_random.uniform(
+                    0.7 * lane.speed_limit, 0.8 * lane.speed_limit
+                )
             else:
-                speed = road.np_random.uniform(Vehicle.DEFAULT_INITIAL_SPEEDS[0], Vehicle.DEFAULT_INITIAL_SPEEDS[1])
-        default_spacing = 12+1.0*speed
-        offset = spacing * default_spacing * np.exp(-5 / 40 * len(road.network.graph[_from][_to]))
-        x0 = np.max([lane.local_coordinates(v.position)[0] for v in road.vehicles]) \
-            if len(road.vehicles) else 3*offset
+                speed = road.np_random.uniform(
+                    Vehicle.DEFAULT_INITIAL_SPEEDS[0], Vehicle.DEFAULT_INITIAL_SPEEDS[1]
+                )
+        default_spacing = 12 + 1.0 * speed
+        offset = (
+            spacing
+            * default_spacing
+            * np.exp(-5 / 40 * len(road.network.graph[_from][_to]))
+        )
+        x0 = (
+            np.max([lane.local_coordinates(v.position)[0] for v in road.vehicles])
+            if len(road.vehicles)
+            else 3 * offset
+        )
         x0 += offset * road.np_random.uniform(0.9, 1.1)
         v = cls(road, lane.position(x0, 0), lane.heading_at(x0), speed)
         return v

@@ -395,7 +414,7 @@ 
Source code for highway_env.vehicle.kinematics
        :return: a new vehicle at the same dynamical state
         """
         v = cls(vehicle.road, vehicle.position, vehicle.heading, vehicle.speed)
-        if hasattr(vehicle, 'color'):
+        if hasattr(vehicle, "color"):
             v.color = vehicle.color
         return v

 
@@ -425,43 +444,52 @@ 
Source code for highway_env.vehicle.kinematics
        :param dt: timestep of integration of the model [s]
         """
         self.clip_actions()
-        delta_f = self.action['steering']
+        delta_f = self.action["steering"]
         beta = np.arctan(1 / 2 * np.tan(delta_f))
-        v = self.speed * np.array([np.cos(self.heading + beta),
-                                   np.sin(self.heading + beta)])
+        v = self.speed * np.array(
+            [np.cos(self.heading + beta), np.sin(self.heading + beta)]
+        )
         self.position += v * dt
         if self.impact is not None:
             self.position += self.impact
             self.crashed = True
             self.impact = None
         self.heading += self.speed * np.sin(beta) / (self.LENGTH / 2) * dt
-        self.speed += self.action['acceleration'] * dt
+        self.speed += self.action["acceleration"] * dt
         self.on_state_update()

 
 
     def clip_actions(self) -> None:
         if self.crashed:
-            self.action['steering'] = 0
-            self.action['acceleration'] = -1.0*self.speed
-        self.action['steering'] = float(self.action['steering'])
-        self.action['acceleration'] = float(self.action['acceleration'])
+            self.action["steering"] = 0
+            self.action["acceleration"] = -1.0 * self.speed
+        self.action["steering"] = float(self.action["steering"])
+        self.action["acceleration"] = float(self.action["acceleration"])
         if self.speed > self.MAX_SPEED:
-            self.action['acceleration'] = min(self.action['acceleration'], 1.0 * (self.MAX_SPEED - self.speed))
+            self.action["acceleration"] = min(
+                self.action["acceleration"], 1.0 * (self.MAX_SPEED - self.speed)
+            )
         elif self.speed < self.MIN_SPEED:
-            self.action['acceleration'] = max(self.action['acceleration'], 1.0 * (self.MIN_SPEED - self.speed))
+            self.action["acceleration"] = max(
+                self.action["acceleration"], 1.0 * (self.MIN_SPEED - self.speed)
+            )
 
     def on_state_update(self) -> None:
         if self.road:
-            self.lane_index = self.road.network.get_closest_lane_index(self.position, self.heading)
+            self.lane_index = self.road.network.get_closest_lane_index(
+                self.position, self.heading
+            )
             self.lane = self.road.network.get_lane(self.lane_index)
             if self.road.record_history:
                 self.history.appendleft(self.create_from(self))
 
-    def predict_trajectory_constant_speed(self, times: np.ndarray) -> Tuple[List[np.ndarray], List[float]]:
-        if self.prediction_type == 'zero_steering':
-            action = {'acceleration': 0.0, 'steering': 0.0}
-        elif self.prediction_type == 'constant_steering':
-            action = {'acceleration': 0.0, 'steering': self.action['steering']}
+    def predict_trajectory_constant_speed(
+        self, times: np.ndarray
+    ) -> Tuple[List[np.ndarray], List[float]]:
+        if self.prediction_type == "zero_steering":
+            action = {"acceleration": 0.0, "steering": 0.0}
+        elif self.prediction_type == "constant_steering":
+            action = {"acceleration": 0.0, "steering": self.action["steering"]}
         else:
             raise ValueError("Unknown predition type")
 
@@ -485,7 +513,11 @@ 
Source code for highway_env.vehicle.kinematics
def destination(self) -> np.ndarray:
         if getattr(self, "route", None):
             last_lane_index = self.route[-1]
-            last_lane_index = last_lane_index if last_lane_index[-1] is not None else (*last_lane_index[:-1], 0)
+            last_lane_index = (
+                last_lane_index
+                if last_lane_index[-1] is not None
+                else (*last_lane_index[:-1], 0)
+            )
             last_lane = self.road.network.get_lane(last_lane_index)
             return last_lane.position(last_lane.length, 0)
         else:
@@ -494,7 +526,9 @@ 
Source code for highway_env.vehicle.kinematics
@property
     def destination_direction(self) -> np.ndarray:
         if (self.destination != self.position).any():
-            return (self.destination - self.position) / np.linalg.norm(self.destination - self.position)
+            return (self.destination - self.position) / np.linalg.norm(
+                self.destination - self.position
+            )
         else:
             return np.zeros((2,))
 
@@ -507,40 +541,49 @@ 
Source code for highway_env.vehicle.kinematics
else:
             return np.zeros((3,))
 
-    def to_dict(self, origin_vehicle: "Vehicle" = None, observe_intentions: bool = True) -> dict:
+    def to_dict(
+        self, origin_vehicle: "Vehicle" = None, observe_intentions: bool = True
+    ) -> dict:
         d = {
-            'presence': 1,
-            'x': self.position[0],
-            'y': self.position[1],
-            'vx': self.velocity[0],
-            'vy': self.velocity[1],
-            'heading': self.heading,
-            'cos_h': self.direction[0],
-            'sin_h': self.direction[1],
-            'cos_d': self.destination_direction[0],
-            'sin_d': self.destination_direction[1],
-            'long_off': self.lane_offset[0],
-            'lat_off': self.lane_offset[1],
-            'ang_off': self.lane_offset[2],
+            "presence": 1,
+            "x": self.position[0],
+            "y": self.position[1],
+            "vx": self.velocity[0],
+            "vy": self.velocity[1],
+            "heading": self.heading,
+            "cos_h": self.direction[0],
+            "sin_h": self.direction[1],
+            "cos_d": self.destination_direction[0],
+            "sin_d": self.destination_direction[1],
+            "long_off": self.lane_offset[0],
+            "lat_off": self.lane_offset[1],
+            "ang_off": self.lane_offset[2],
         }
         if not observe_intentions:
             d["cos_d"] = d["sin_d"] = 0
         if origin_vehicle:
             origin_dict = origin_vehicle.to_dict()
-            for key in ['x', 'y', 'vx', 'vy']:
+            for key in ["x", "y", "vx", "vy"]:
                 d[key] -= origin_dict[key]
         return d
 
     def __str__(self):
-        return "{} #{}: {}".format(self.__class__.__name__, id(self) % 1000, self.position)
+        return "{} #{}: {}".format(
+            self.__class__.__name__, id(self) % 1000, self.position
+        )
 
     def __repr__(self):
         return self.__str__()
 
 

 [docs]
-    def predict_trajectory(self, actions: List, action_duration: float, trajectory_timestep: float, dt: float) \
-            -> List['Vehicle']:
+    def predict_trajectory(
+        self,
+        actions: List,
+        action_duration: float,
+        trajectory_timestep: float,
+        dt: float,
+    ) -> List["Vehicle"]:
         """
         Predict the future trajectory of the vehicle given a sequence of actions.
 
diff --git a/main/observations/index.html b/main/observations/index.html
index f242e39ae..fd365171e 100644
--- a/main/observations/index.html
+++ b/main/observations/index.html
@@ -557,25 +557,25 @@ 
Example configuration
 

 

-
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+
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    0.        ]
- [ 1.          0.20323725  0.04       -0.07361443  0.          1.
+ [ 1.          0.2116441  -0.04       -0.07987082  0.          1.
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- [ 1.          0.39618185  0.08       -0.11105874  0.          1.
+ [ 1.          0.40037498 -0.04       -0.14375238  0.          1.
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- [ 1.          0.6169058   0.04       -0.17033932  0.          1.
+ [ 1.          0.5924493  -0.04       -0.14188124  0.          1.
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- [ 1.          0.83093834  0.08       -0.0526074   0.          1.
+ [ 1.          0.81080985  0.         -0.17013432  0.          1.
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- [ 1.          1.          0.12       -0.18704522  0.          1.
+ [ 1.          1.          0.04       -0.12747008  0.          1.
    0.        ]
- [ 1.          1.          0.04       -0.1492452   0.          1.
+ [ 1.          1.          0.04       -0.08804176  0.          1.
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- [ 1.          1.          0.08       -0.05019206  0.          1.
+ [ 1.          1.          0.         -0.11419602  0.          1.
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- [ 1.          1.          0.         -0.07072275  0.          1.
+ [ 1.          1.          0.         -0.13449906  0.          1.
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- [ 1.          1.          0.08       -0.18716832  0.          1.
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diff --git a/main/searchindex.js b/main/searchindex.js
index 7590d8d4a..827b5ae39 100644
--- a/main/searchindex.js
+++ b/main/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["404", "actions/index", "bibliography/index", "dynamics/index", "dynamics/road/lane", "dynamics/road/regulation", "dynamics/road/road", "dynamics/vehicle/behavior", "dynamics/vehicle/controller", "dynamics/vehicle/kinematics", "environments/highway", "environments/index", "environments/intersection", "environments/merge", "environments/parking", "environments/racetrack", "environments/roundabout", "faq", "graphics/index", "index", "installation", "make_your_own", "multi_agent", "observations/index", "quickstart", "rewards/index", "user_guide"], "filenames": ["404.md", "actions/index.md", "bibliography/index.md", "dynamics/index.md", "dynamics/road/lane.md", "dynamics/road/regulation.md", "dynamics/road/road.md", "dynamics/vehicle/behavior.md", "dynamics/vehicle/controller.md", "dynamics/vehicle/kinematics.md", "environments/highway.md", "environments/index.md", "environments/intersection.md", "environments/merge.md", "environments/parking.md", "environments/racetrack.md", "environments/roundabout.md", "faq.md", "graphics/index.md", "index.md", "installation.md", "make_your_own.md", "multi_agent.md", "observations/index.md", "quickstart.md", "rewards/index.md", "user_guide.md"], "titles": ["404", "Actions", "Bibliography", "Dynamics", "Lane", "Road regulation", "Road", "Behavior", "Control", "Kinematics", "Highway", "The environments", "Intersection", "Merge", "Parking", "Racetrack", "Roundabout", "Frequently Asked Questions", "Graphics", "Welcome to highway-env\u2019s documentation!", "Installation", "Make your own environment", "The Multi-Agent setting", "Observations", "Getting Started", "Rewards", "User Guide"], "terms": {"similarli": 1, "observ": [1, 2, 10, 12, 13, 14, 15, 16, 21, 24, 25, 26], "sever": [1, 7, 10, 12, 17, 21, 23, 24], "type": [1, 4, 10, 12, 13, 14, 15, 16, 21, 22, 23, 24], "can": [1, 4, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25], "us": [1, 7, 8, 9, 14, 18, 19, 20, 21, 23, 24, 25], 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\ No newline at end of file
+Search.setIndex({"docnames": ["404", "actions/index", "bibliography/index", "dynamics/index", "dynamics/road/lane", "dynamics/road/regulation", "dynamics/road/road", "dynamics/vehicle/behavior", "dynamics/vehicle/controller", "dynamics/vehicle/kinematics", "environments/highway", "environments/index", "environments/intersection", "environments/merge", "environments/parking", "environments/racetrack", "environments/roundabout", "faq", "graphics/index", "index", "installation", "make_your_own", "multi_agent", "observations/index", "quickstart", "rewards/index", "user_guide"], "filenames": ["404.md", "actions/index.md", "bibliography/index.md", "dynamics/index.md", "dynamics/road/lane.md", "dynamics/road/regulation.md", "dynamics/road/road.md", "dynamics/vehicle/behavior.md", "dynamics/vehicle/controller.md", "dynamics/vehicle/kinematics.md", "environments/highway.md", "environments/index.md", "environments/intersection.md", "environments/merge.md", "environments/parking.md", "environments/racetrack.md", "environments/roundabout.md", "faq.md", "graphics/index.md", "index.md", "installation.md", "make_your_own.md", "multi_agent.md", "observations/index.md", "quickstart.md", "rewards/index.md", "user_guide.md"], "titles": ["404", "Actions", "Bibliography", "Dynamics", "Lane", "Road regulation", "Road", "Behavior", "Control", "Kinematics", "Highway", "The environments", "Intersection", "Merge", "Parking", "Racetrack", "Roundabout", "Frequently Asked Questions", "Graphics", "Welcome to highway-env\u2019s documentation!", "Installation", "Make your own environment", "The Multi-Agent setting", "Observations", "Getting Started", "Rewards", "User Guide"], "terms": {"similarli": 1, "observ": [1, 2, 10, 12, 13, 14, 15, 16, 21, 24, 25, 26], "sever": [1, 7, 10, 12, 17, 21, 23, 24], "type": [1, 4, 10, 12, 13, 14, 15, 16, 21, 22, 23, 24], "can": [1, 4, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25], "us": [1, 7, 8, 9, 14, 18, 19, 20, 21, 23, 24, 25], 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\ No newline at end of file