From 7acaa6bf54c2b75f1ca53f8e329eeaa825d5c63c Mon Sep 17 00:00:00 2001 From: Jannis Becktepe <61006252+becktepe@users.noreply.github.com> Date: Tue, 4 Jun 2024 16:50:29 +0200 Subject: [PATCH] feat: Evaluation script --- arlbench/autorl/autorl_env.py | 11 +- arlbench/autorl/checkpointing.py | 5 +- arlbench/core/algorithms/__init__.py | 35 ++- arlbench/core/algorithms/buffers.py | 9 +- arlbench/core/algorithms/dqn/__init__.py | 10 +- arlbench/core/algorithms/dqn/dqn.py | 22 +- arlbench/core/algorithms/ppo/__init__.py | 10 +- arlbench/core/algorithms/ppo/ppo.py | 13 +- .../algorithms/prioritised_item_buffer.py | 9 +- arlbench/core/algorithms/sac/__init__.py | 10 +- arlbench/core/algorithms/sac/sac.py | 54 +++-- arlbench/utils/__init__.py | 11 +- docs/basic_usage/objectives.rst | 2 +- docs/images/structure.png | Bin 171105 -> 256969 bytes docs/images/subsets.png | Bin 320169 -> 243769 bytes examples/Readme.md | 24 +- .../environment/atari_battle_zone.yaml | 12 + .../environment/box2d_bipedal_walker.yaml | 8 + .../environment/box2d_lunar_lander.yaml | 8 + .../configs/environment/brax_halfcheetah.yaml | 8 + examples/configs/environment/cc_acrobot.yaml | 8 + .../cc_continuous_mountain_car.yaml | 8 + examples/configs/environment/cc_pendulum.yaml | 8 + .../configs/environment/xland_door_key.yaml | 8 + .../environment/xland_empty_random.yaml | 8 + .../configs/environment/xland_four_rooms.yaml | 8 + examples/configs/evaluate.yaml | 38 +++ .../incumbent/dqn_acrobot_my_optimizer.yaml | 19 ++ .../dqn_battle_zone_my_optimizer.yaml | 24 ++ .../incumbent/dqn_door_key_my_optimizer.yaml | 19 ++ .../dqn_four_rooms_my_optimizer.yaml | 19 ++ .../ppo_battle_zone_my_optimizer.yaml | 24 ++ .../ppo_empty_random_my_optimizer.yaml | 19 ++ .../ppo_four_rooms_my_optimizer.yaml | 19 ++ .../ppo_halfcheetah_my_optimizer.yaml | 23 ++ .../ppo_lunar_lander_my_optimizer.yaml | 19 ++ .../sac_bipedal_walker_my_optimizer.yaml | 18 ++ ..._continuous_mountain_car_my_optimizer.yaml | 18 ++ .../sac_halfcheetah_my_optimizer.yaml | 23 ++ .../incumbent/sac_pendulum_my_optimizer.yaml | 18 ++ .../dqn_CartPole-v1/100/multirun.yaml | 216 +++++++++++++++++ .../dqn_Acrobot-v1/100/multirun.yaml | 217 ++++++++++++++++++ examples/run_arlbench.py | 13 -- examples/run_heuristic_schedule.py | 2 +- examples/run_reactive_schedule.py | 4 +- run_arlbench.py | 13 -- tests/autorl/test_autorl_env.py | 1 - tests/autorl/test_checkpointing.py | 5 +- tests/autorl/test_objectives.py | 3 +- tests/core/algorithms/test_dqn.py | 1 - tests/core/algorithms/test_ppo.py | 1 - tests/core/algorithms/test_sac.py | 2 - tests/utils/test_utils.py | 3 +- 53 files changed, 982 insertions(+), 108 deletions(-) create mode 100644 examples/configs/environment/atari_battle_zone.yaml create mode 100644 examples/configs/environment/box2d_bipedal_walker.yaml create mode 100644 examples/configs/environment/box2d_lunar_lander.yaml create mode 100644 examples/configs/environment/brax_halfcheetah.yaml create mode 100644 examples/configs/environment/cc_acrobot.yaml create mode 100644 examples/configs/environment/cc_continuous_mountain_car.yaml create mode 100644 examples/configs/environment/cc_pendulum.yaml create mode 100644 examples/configs/environment/xland_door_key.yaml create mode 100644 examples/configs/environment/xland_empty_random.yaml create mode 100644 examples/configs/environment/xland_four_rooms.yaml create mode 100644 examples/configs/evaluate.yaml create mode 100644 examples/configs/incumbent/dqn_acrobot_my_optimizer.yaml create mode 100644 examples/configs/incumbent/dqn_battle_zone_my_optimizer.yaml create mode 100644 examples/configs/incumbent/dqn_door_key_my_optimizer.yaml create mode 100644 examples/configs/incumbent/dqn_four_rooms_my_optimizer.yaml create mode 100644 examples/configs/incumbent/ppo_battle_zone_my_optimizer.yaml create mode 100644 examples/configs/incumbent/ppo_empty_random_my_optimizer.yaml create mode 100644 examples/configs/incumbent/ppo_four_rooms_my_optimizer.yaml create mode 100644 examples/configs/incumbent/ppo_halfcheetah_my_optimizer.yaml create mode 100644 examples/configs/incumbent/ppo_lunar_lander_my_optimizer.yaml create mode 100644 examples/configs/incumbent/sac_bipedal_walker_my_optimizer.yaml create mode 100644 examples/configs/incumbent/sac_continuous_mountain_car_my_optimizer.yaml create mode 100644 examples/configs/incumbent/sac_halfcheetah_my_optimizer.yaml create mode 100644 examples/configs/incumbent/sac_pendulum_my_optimizer.yaml create mode 100644 examples/evaluation/default_method/dqn_CartPole-v1/100/multirun.yaml create mode 100644 examples/evaluation/my_optimizer/dqn_Acrobot-v1/100/multirun.yaml diff --git a/arlbench/autorl/autorl_env.py b/arlbench/autorl/autorl_env.py index 58b9991c4..2989017fe 100644 --- a/arlbench/autorl/autorl_env.py +++ b/arlbench/autorl/autorl_env.py @@ -9,8 +9,15 @@ import numpy as np import pandas as pd -from arlbench.core.algorithms import (DQN, PPO, SAC, Algorithm, AlgorithmState, - TrainResult, TrainReturnT) +from arlbench.core.algorithms import ( + DQN, + PPO, + SAC, + Algorithm, + AlgorithmState, + TrainResult, + TrainReturnT, +) from arlbench.core.environments import make_env from arlbench.utils import config_space_to_gymnasium_space diff --git a/arlbench/autorl/checkpointing.py b/arlbench/autorl/checkpointing.py index eb615d6fb..a14b761e0 100644 --- a/arlbench/autorl/checkpointing.py +++ b/arlbench/autorl/checkpointing.py @@ -11,8 +11,9 @@ import jax.numpy as jnp import numpy as np import orbax.checkpoint as ocp -from flashbax.buffers.prioritised_trajectory_buffer import \ - PrioritisedTrajectoryBufferState +from flashbax.buffers.prioritised_trajectory_buffer import ( + PrioritisedTrajectoryBufferState, +) from flashbax.buffers.sum_tree import SumTreeState from flashbax.vault import Vault from flax.core.frozen_dict import FrozenDict diff --git a/arlbench/core/algorithms/__init__.py b/arlbench/core/algorithms/__init__.py index 02dca904a..2330d5335 100644 --- a/arlbench/core/algorithms/__init__.py +++ b/arlbench/core/algorithms/__init__.py @@ -1,16 +1,35 @@ from collections.abc import Callable from typing import Optional, Union -from flashbax.buffers.prioritised_trajectory_buffer import \ - PrioritisedTrajectoryBufferState +from flashbax.buffers.prioritised_trajectory_buffer import ( + PrioritisedTrajectoryBufferState, +) from .algorithm import Algorithm -from .dqn import (DQN, DQNMetrics, DQNRunnerState, DQNState, DQNTrainingResult, - DQNTrainReturnT) -from .ppo import (PPO, PPOMetrics, PPORunnerState, PPOState, PPOTrainingResult, - PPOTrainReturnT) -from .sac import (SAC, SACMetrics, SACRunnerState, SACState, SACTrainingResult, - SACTrainReturnT) +from .dqn import ( + DQN, + DQNMetrics, + DQNRunnerState, + DQNState, + DQNTrainingResult, + DQNTrainReturnT, +) +from .ppo import ( + PPO, + PPOMetrics, + PPORunnerState, + PPOState, + PPOTrainingResult, + PPOTrainReturnT, +) +from .sac import ( + SAC, + SACMetrics, + SACRunnerState, + SACState, + SACTrainingResult, + SACTrainReturnT, +) TrainResult = Union[DQNTrainingResult, PPOTrainingResult, SACTrainingResult] TrainMetrics = Union[DQNMetrics, PPOMetrics, SACMetrics] diff --git a/arlbench/core/algorithms/buffers.py b/arlbench/core/algorithms/buffers.py index 6baa7532e..977ff16af 100644 --- a/arlbench/core/algorithms/buffers.py +++ b/arlbench/core/algorithms/buffers.py @@ -8,9 +8,12 @@ from flashbax import utils from flashbax.buffers import sum_tree from flashbax.buffers.prioritised_trajectory_buffer import ( - Experience, PrioritisedTrajectoryBufferSample, - PrioritisedTrajectoryBufferState, _get_sample_trajectories, - get_invalid_indices) + Experience, + PrioritisedTrajectoryBufferSample, + PrioritisedTrajectoryBufferState, + _get_sample_trajectories, + get_invalid_indices, +) from flashbax.buffers.trajectory_buffer import calculate_uniform_item_indices if TYPE_CHECKING: diff --git a/arlbench/core/algorithms/dqn/__init__.py b/arlbench/core/algorithms/dqn/__init__.py index 8ecce1b47..ff610a6c2 100644 --- a/arlbench/core/algorithms/dqn/__init__.py +++ b/arlbench/core/algorithms/dqn/__init__.py @@ -1,5 +1,11 @@ -from .dqn import (DQN, DQNMetrics, DQNRunnerState, DQNState, DQNTrainingResult, - DQNTrainReturnT) +from .dqn import ( + DQN, + DQNMetrics, + DQNRunnerState, + DQNState, + DQNTrainingResult, + DQNTrainReturnT, +) __all__ = [ "DQN", diff --git a/arlbench/core/algorithms/dqn/dqn.py b/arlbench/core/algorithms/dqn/dqn.py index 776101bd0..a6d28f1e4 100644 --- a/arlbench/core/algorithms/dqn/dqn.py +++ b/arlbench/core/algorithms/dqn/dqn.py @@ -10,23 +10,31 @@ import jax.numpy as jnp import numpy as np import optax -from ConfigSpace import (Categorical, Configuration, ConfigurationSpace, - EqualsCondition, Float, Integer) +from ConfigSpace import ( + Categorical, + Configuration, + ConfigurationSpace, + EqualsCondition, + Float, + Integer, +) from flax.training.train_state import TrainState from arlbench.core import running_statistics from arlbench.core.algorithms.algorithm import Algorithm from arlbench.core.algorithms.buffers import uniform_sample from arlbench.core.algorithms.common import TimeStep -from arlbench.core.algorithms.prioritised_item_buffer import \ - make_prioritised_item_buffer +from arlbench.core.algorithms.prioritised_item_buffer import ( + make_prioritised_item_buffer, +) from .models import CNNQ, MLPQ if TYPE_CHECKING: import chex - from flashbax.buffers.prioritised_trajectory_buffer import \ - PrioritisedTrajectoryBufferState + from flashbax.buffers.prioritised_trajectory_buffer import ( + PrioritisedTrajectoryBufferState, + ) from flax.core.frozen_dict import FrozenDict from arlbench.core.environments import Environment @@ -780,7 +788,7 @@ def do_update( buffer_state (PrioritisedTrajectoryBufferState): Buffer state. Returns: - tuple[chex.PRNGKey, DQNTrainState, PrioritisedTrajectoryBufferState, DQNMetrics]: + tuple[chex.PRNGKey, DQNTrainState, PrioritisedTrajectoryBufferState, DQNMetrics]: Random number generator key, training state, buffer state, and metrics. """ diff --git a/arlbench/core/algorithms/ppo/__init__.py b/arlbench/core/algorithms/ppo/__init__.py index 8964c726f..67c3d4c72 100644 --- a/arlbench/core/algorithms/ppo/__init__.py +++ b/arlbench/core/algorithms/ppo/__init__.py @@ -1,5 +1,11 @@ -from .ppo import (PPO, PPOMetrics, PPORunnerState, PPOState, PPOTrainingResult, - PPOTrainReturnT) +from .ppo import ( + PPO, + PPOMetrics, + PPORunnerState, + PPOState, + PPOTrainingResult, + PPOTrainReturnT, +) __all__ = [ "PPO", diff --git a/arlbench/core/algorithms/ppo/ppo.py b/arlbench/core/algorithms/ppo/ppo.py index e717a4728..7487cbab2 100644 --- a/arlbench/core/algorithms/ppo/ppo.py +++ b/arlbench/core/algorithms/ppo/ppo.py @@ -10,8 +10,7 @@ import jax.numpy as jnp import numpy as np import optax -from ConfigSpace import (Categorical, Configuration, ConfigurationSpace, Float, - Integer) +from ConfigSpace import Categorical, Configuration, ConfigurationSpace, Float, Integer from flax.training.train_state import TrainState from arlbench.core import running_statistics @@ -646,13 +645,13 @@ def _update_epoch( """One epoch of network updates using minibatches of the current transition batch. Args: - update_state (tuple[PPOTrainState, Transition, jnp.ndarray, jnp.ndarray, chex.PRNGKey]): + update_state (tuple[PPOTrainState, Transition, jnp.ndarray, jnp.ndarray, chex.PRNGKey]): (train_state, transition_batch, advantages, targets, rng) Current update state. _ (None): Unused parameter (required for jax.lax.scan). Returns: tuple[tuple[PPOTrainState, Transition, jnp.ndarray, jnp.ndarray, chex.PRNGKey], - tuple[tuple | None, tuple | None]]: Tuple of (train_state, transition_batch, + tuple[tuple | None, tuple | None]]: Tuple of (train_state, transition_batch, advantages, targets, rng) and (loss, grads) if tracked. """ train_state, traj_batch, advantages, targets, rng = update_state @@ -715,11 +714,11 @@ def _update_minibatch( Args: train_state (PPOTrainState): PPO training state. - batch_info (tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]): + batch_info (tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]): Minibatch of transitions, advantages and targets. Returns: - tuple[PPOTrainState, tuple[tuple | None, tuple | None]]: + tuple[PPOTrainState, tuple[tuple | None, tuple | None]]: Tuple of PPO train state and (loss, grads) if tracked. """ traj_batch, advantages, targets = batch_info @@ -749,7 +748,7 @@ def _loss_fn( targets (jnp.ndarray): Targets. Returns: - tuple[jnp.ndarray, tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]]: + tuple[jnp.ndarray, tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]]: Tuple of (total_loss, (value_loss, actor_loss, entropy)). """ # Rerun network diff --git a/arlbench/core/algorithms/prioritised_item_buffer.py b/arlbench/core/algorithms/prioritised_item_buffer.py index e17a49d9b..43b4abbd4 100644 --- a/arlbench/core/algorithms/prioritised_item_buffer.py +++ b/arlbench/core/algorithms/prioritised_item_buffer.py @@ -20,9 +20,12 @@ from flashbax.buffers.item_buffer import validate_item_buffer_args from flashbax.buffers.prioritised_flat_buffer import validate_priority_exponent from flashbax.buffers.prioritised_trajectory_buffer import ( - PrioritisedTrajectoryBuffer, PrioritisedTrajectoryBufferSample, - PrioritisedTrajectoryBufferState, make_prioritised_trajectory_buffer, - validate_device) + PrioritisedTrajectoryBuffer, + PrioritisedTrajectoryBufferSample, + PrioritisedTrajectoryBufferState, + make_prioritised_trajectory_buffer, + validate_device, +) from flashbax.utils import add_dim_to_args if TYPE_CHECKING: diff --git a/arlbench/core/algorithms/sac/__init__.py b/arlbench/core/algorithms/sac/__init__.py index c0313ca26..8572fb869 100644 --- a/arlbench/core/algorithms/sac/__init__.py +++ b/arlbench/core/algorithms/sac/__init__.py @@ -1,5 +1,11 @@ -from .sac import (SAC, SACMetrics, SACRunnerState, SACState, SACTrainingResult, - SACTrainReturnT) +from .sac import ( + SAC, + SACMetrics, + SACRunnerState, + SACState, + SACTrainingResult, + SACTrainReturnT, +) __all__ = [ "SAC", diff --git a/arlbench/core/algorithms/sac/sac.py b/arlbench/core/algorithms/sac/sac.py index 3705472ae..9d02b5501 100644 --- a/arlbench/core/algorithms/sac/sac.py +++ b/arlbench/core/algorithms/sac/sac.py @@ -10,24 +10,38 @@ import jax.numpy as jnp import numpy as np import optax -from ConfigSpace import (Categorical, Configuration, ConfigurationSpace, - EqualsCondition, Float, Integer) +from ConfigSpace import ( + Categorical, + Configuration, + ConfigurationSpace, + EqualsCondition, + Float, + Integer, +) from flax.training.train_state import TrainState from arlbench.core import running_statistics from arlbench.core.algorithms.algorithm import Algorithm from arlbench.core.algorithms.buffers import uniform_sample from arlbench.core.algorithms.common import TimeStep -from arlbench.core.algorithms.prioritised_item_buffer import \ - make_prioritised_item_buffer - -from .models import (AlphaCoef, SACCNNActor, SACCNNCritic, SACMLPActor, - SACMLPCritic, SACVectorCritic) +from arlbench.core.algorithms.prioritised_item_buffer import ( + make_prioritised_item_buffer, +) + +from .models import ( + AlphaCoef, + SACCNNActor, + SACCNNCritic, + SACMLPActor, + SACMLPCritic, + SACVectorCritic, +) if TYPE_CHECKING: import chex - from flashbax.buffers.prioritised_trajectory_buffer import \ - PrioritisedTrajectoryBufferState + from flashbax.buffers.prioritised_trajectory_buffer import ( + PrioritisedTrajectoryBufferState, + ) from flax.core.frozen_dict import FrozenDict from arlbench.core.environments import Environment @@ -134,14 +148,14 @@ def __init__( Args: hpo_config (Configuration): Hyperparameter configuration. env (Environment | AutoRLWrapper): Training environment. - eval_env (Environment | AutoRLWrapper | None, optional): Evaluation environent + eval_env (Environment | AutoRLWrapper | None, optional): Evaluation environent (otherwise training environment is used for evaluation). Defaults to None. cnn_policy (bool, optional): Use CNN network architecture. Defaults to False. - nas_config (Configuration | None, optional): Neural architecture + nas_config (Configuration | None, optional): Neural architecture configuration. Defaults to None. - track_trajectories (bool, optional): Track metrics such as loss and gradients + track_trajectories (bool, optional): Track metrics such as loss and gradients during training. Defaults to False. - track_metrics (bool, optional): Track trajectories during training. + track_metrics (bool, optional): Track trajectories during training. Defaults to False. """ if nas_config is None: @@ -610,7 +624,7 @@ def update_critic( rng (chex.PRNGKey): Random number generator key. Returns: - tuple[SACTrainState, jnp.ndarray, jnp.ndarray, FrozenDict, chex.PRNGKey]: + tuple[SACTrainState, jnp.ndarray, jnp.ndarray, FrozenDict, chex.PRNGKey]: Updated training state and metrics. """ rng, action_rng = jax.random.split(rng, 2) @@ -690,7 +704,7 @@ def actor_loss( alpha_params (FrozenDict): Alpha network parameters. Returns: - tuple[jnp.ndarray, jnp.ndarray]: Update training state and metrics. + tuple[jnp.ndarray, jnp.ndarray]: Update training state and metrics. """ pi = self.actor_network.apply(actor_params, experience.last_obs) actor_actions, log_prob = pi.sample_and_log_prob(seed=action_rng) @@ -756,7 +770,7 @@ def _update_step( _ (None): Unused parameter. Returns: - tuple[ tuple[SACRunnerState, PrioritisedTrajectoryBufferState], + tuple[ tuple[SACRunnerState, PrioritisedTrajectoryBufferState], tuple[SACMetrics | None, Transition | None], ]: Updated training state and metrics. """ @@ -785,7 +799,7 @@ def do_update( buffer_state (PrioritisedTrajectoryBufferState): Buffer state. Returns: - tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, + tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, PrioritisedTrajectoryBufferState, SACMetrics]: Updated training states and metrics. """ @@ -811,12 +825,12 @@ def gradient_step( """Perform a gradient update step. Args: - carry (tuple[ chex.PRNGKey, SACTrainState, SACTrainState, + carry (tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, PrioritisedTrajectoryBufferState, ]): Carry for jax.lax.scan(): _ (None): Unused parameter. Returns: - tuple[ tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, + tuple[ tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, PrioritisedTrajectoryBufferState, ], SACMetrics, ]: Updated training states and metrics. """ ( @@ -944,7 +958,7 @@ def dont_update( buffer_state (PrioritisedTrajectoryBufferState): Buffer state. Returns: - tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, + tuple[ chex.PRNGKey, SACTrainState, SACTrainState, SACTrainState, PrioritisedTrajectoryBufferState, SACMetrics]: Input training states and metrics. """ single_loss = jnp.array( diff --git a/arlbench/utils/__init__.py b/arlbench/utils/__init__.py index f08ca65db..e5e7798d8 100644 --- a/arlbench/utils/__init__.py +++ b/arlbench/utils/__init__.py @@ -1,6 +1,11 @@ -from .common import (config_space_to_gymnasium_space, config_space_to_yaml, - gymnasium_space_to_gymnax_space, recursive_concat, - save_defaults_to_yaml, tuple_concat) +from .common import ( + config_space_to_gymnasium_space, + config_space_to_yaml, + gymnasium_space_to_gymnax_space, + recursive_concat, + save_defaults_to_yaml, + tuple_concat, +) __all__ = [ "config_space_to_gymnasium_space", diff --git a/docs/basic_usage/objectives.rst b/docs/basic_usage/objectives.rst index 1a59039a6..a4aaeabed 100644 --- a/docs/basic_usage/objectives.rst +++ b/docs/basic_usage/objectives.rst @@ -12,4 +12,4 @@ The following objectives are available at the moment: - reward_mean: the mean evaluation reward across a number of evaluation episodes - reward_std: the standard deviation of the 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Examples for black-box hyperparameter tuning using the 'hypersweeper' package. + +1. Examples for black-box hyperparameter tuning using the 'hypersweeper' package. 2. Running a schedule based on a reward heuristic 3. Running a reactive schedule based on the gradient history +Additionally, we provide instructions on how to evaluate an incumbent on ARLBench: + +4. Evaluating a HPO result + We use 'hydra' as a command line interface for these experiments, you'll find the corresponding configurations (including some variations on the algorithms and environments) in the 'configs' directory. The "hypersweeper_tuning" and "schedules" notebooks can help you run these examples and inspect their results in an interactive way. @@ -72,6 +77,7 @@ python run_arlbench.py --config-name=smac -m environment=cc_cartpole algorithm=p ``` The search space specification works very similarly via a yaml file, 'configs/search_space/cc_ppo.yaml' contains: + ```yaml seed: 0 hyperparameters: @@ -82,7 +88,7 @@ hyperparameters: log: true hp_config.ent_coef: type: uniform_float - upper: 0.5 + upper: 0.5 lower: 0.0 log: false hp_config.minibatch_size: @@ -121,7 +127,6 @@ hyperparameters: This config sets a seed for the search space as well as lists the hyperparameters to configure with the values they can take. This way we can configure the full HPO setting with only yaml files to make the process easy to follow and simple to document for others. - ## 2. Heuristic Schedules We can also use ARLBench to dynamically change the hyperparameter config. We provide a simple example for this in 'run_heuristic_schedule.py': as soon as the agent improves over a certain reward threshold, we decrease the exploration epsilon in DQN a bit. This is likely not the best approach in practice, so feel free to play around with this idea! To see the result, run: @@ -155,7 +160,7 @@ autorl: n_eval_episodes: 10 ``` -As you can see, most of the defaults are decided by the environment and algorithm we choose. For dynamic execution, we are interested in the 'n_steps' and 'n_total_timesteps' keys. +As you can see, most of the defaults are decided by the environment and algorithm we choose. For dynamic execution, we are interested in the 'n_steps' and 'n_total_timesteps' keys. 'n_steps' decides how many steps should be taken in the AutoRL Environment - in other words, how many schedule intervals we'd like to have. The 'n_total_timesteps' key then decides the length of each interval. In the current config, we do a single training interval consisting of the total number of environment steps suggested for our target domain. If we want to instead run a schedule of length 10 with each schedule segment taking 10e4 steps, we can change the configuration like this: @@ -177,4 +182,13 @@ To actually configure to what information ARLBench returns about the RL algorith python run_reactive_schedule.py "autorl.state_features=['grad_info']" ``` -Now we can build a schedule that takes the gradient information into account. \ No newline at end of file +Now we can build a schedule that takes the gradient information into account. + +## 4. Evaluation + +### PPO + +```bash +python run_arlbench.py --config-name=evaluate -m "autorl.seed=100,101,102" "+incumbent=glob(*)" + +``` diff --git a/examples/configs/environment/atari_battle_zone.yaml b/examples/configs/environment/atari_battle_zone.yaml new file mode 100644 index 000000000..29258377e --- /dev/null +++ b/examples/configs/environment/atari_battle_zone.yaml @@ -0,0 +1,12 @@ +name: "BattleZone-v5" +framework: "envpool" +n_total_timesteps: 10e6 +kwargs: + episodic_life: True + reward_clip: True +eval_kwargs: + episodic_life: False + reward_clip: False +cnn_policy: True +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/box2d_bipedal_walker.yaml b/examples/configs/environment/box2d_bipedal_walker.yaml new file mode 100644 index 000000000..db96d5d8f --- /dev/null +++ b/examples/configs/environment/box2d_bipedal_walker.yaml @@ -0,0 +1,8 @@ +name: "BipedalWalker-v3" +framework: "envpool" +n_total_timesteps: 1e6 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/box2d_lunar_lander.yaml b/examples/configs/environment/box2d_lunar_lander.yaml new file mode 100644 index 000000000..7af845d58 --- /dev/null +++ b/examples/configs/environment/box2d_lunar_lander.yaml @@ -0,0 +1,8 @@ +name: "LunarLander-v2" +framework: "envpool" +n_total_timesteps: 1e6 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/brax_halfcheetah.yaml b/examples/configs/environment/brax_halfcheetah.yaml new file mode 100644 index 000000000..50edd8e30 --- /dev/null +++ b/examples/configs/environment/brax_halfcheetah.yaml @@ -0,0 +1,8 @@ +name: "halfcheetah" +framework: "brax" +n_total_timesteps: 50000000 +kwargs: {"backend": "mjx"} +eval_kwargs: {"backend": "mjx"} +cnn_policy: False +deterministic_eval: True +n_envs: 2048 diff --git a/examples/configs/environment/cc_acrobot.yaml b/examples/configs/environment/cc_acrobot.yaml new file mode 100644 index 000000000..23516cced --- /dev/null +++ b/examples/configs/environment/cc_acrobot.yaml @@ -0,0 +1,8 @@ +name: "Acrobot-v1" +framework: "gymnax" +n_total_timesteps: 1e5 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/cc_continuous_mountain_car.yaml b/examples/configs/environment/cc_continuous_mountain_car.yaml new file mode 100644 index 000000000..869aaf830 --- /dev/null +++ b/examples/configs/environment/cc_continuous_mountain_car.yaml @@ -0,0 +1,8 @@ +name: "MountainCarContinuous-v0" +framework: "gymnax" +n_total_timesteps: 1e5 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/cc_pendulum.yaml b/examples/configs/environment/cc_pendulum.yaml new file mode 100644 index 000000000..a6dc98a3f --- /dev/null +++ b/examples/configs/environment/cc_pendulum.yaml @@ -0,0 +1,8 @@ +name: "Pendulum-v1" +framework: "gymnax" +n_total_timesteps: 1e5 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/xland_door_key.yaml b/examples/configs/environment/xland_door_key.yaml new file mode 100644 index 000000000..9263e2f1d --- /dev/null +++ b/examples/configs/environment/xland_door_key.yaml @@ -0,0 +1,8 @@ +name: "MiniGrid-DoorKey-5x5" +framework: "xland" +n_total_timesteps: 100000 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/xland_empty_random.yaml b/examples/configs/environment/xland_empty_random.yaml new file mode 100644 index 000000000..1f0e06479 --- /dev/null +++ b/examples/configs/environment/xland_empty_random.yaml @@ -0,0 +1,8 @@ +name: "MiniGrid-EmptyRandom-5x5" +framework: "xland" +n_total_timesteps: 100000 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/environment/xland_four_rooms.yaml b/examples/configs/environment/xland_four_rooms.yaml new file mode 100644 index 000000000..8f1b7c39f --- /dev/null +++ b/examples/configs/environment/xland_four_rooms.yaml @@ -0,0 +1,8 @@ +name: "MiniGrid-FourRooms" +framework: "xland" +n_total_timesteps: 5000000 +kwargs: {} +eval_kwargs: {} +cnn_policy: False +deterministic_eval: True +n_envs: 8 diff --git a/examples/configs/evaluate.yaml b/examples/configs/evaluate.yaml new file mode 100644 index 000000000..14ab0afd0 --- /dev/null +++ b/examples/configs/evaluate.yaml @@ -0,0 +1,38 @@ +defaults: + - _self_ + - /algorithm: dqn + - /environment: cc_cartpole + - /incumbent: dqn_acrobot_my_optimizer + +hydra: + run: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + sweep: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + job: + chdir: true + +jax_enable_x64: true +load_checkpoint: "" +hpo_method: "default_method" + +autorl: + seed: 100 # We use this as the first test seed + env_framework: ${environment.framework} + env_name: ${environment.name} + env_kwargs: ${environment.kwargs} + eval_env_kwargs: ${environment.eval_kwargs} + n_envs: ${environment.n_envs} + algorithm: ${algorithm} + cnn_policy: ${environment.cnn_policy} + nas_config: ${nas_config} + n_total_timesteps: ${environment.n_total_timesteps} + checkpoint: [] + checkpoint_name: "default_checkpoint" + checkpoint_dir: "/tmp" + state_features: [] + objectives: ["reward_mean"] + optimize_objectives: "upper" + n_steps: 1 + n_eval_steps: 100 + n_eval_episodes: 10 diff --git a/examples/configs/incumbent/dqn_acrobot_my_optimizer.yaml b/examples/configs/incumbent/dqn_acrobot_my_optimizer.yaml new file mode 100644 index 000000000..139fee9a0 --- /dev/null +++ b/examples/configs/incumbent/dqn_acrobot_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: cc_acrobot + - override /algorithm: dqn + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 64 + buffer_size: 100000 + buffer_prio_sampling: false + initial_epsilon: 0.64 + target_epsilon: 0.112 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 diff --git a/examples/configs/incumbent/dqn_battle_zone_my_optimizer.yaml b/examples/configs/incumbent/dqn_battle_zone_my_optimizer.yaml new file mode 100644 index 000000000..e89b7afc7 --- /dev/null +++ b/examples/configs/incumbent/dqn_battle_zone_my_optimizer.yaml @@ -0,0 +1,24 @@ +# @package _global_ +defaults: + - override /environment: atari_battle_zone + - override /algorithm: dqn + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 64 + buffer_size: 100000 + buffer_prio_sampling: false + initial_epsilon: 0.64 + target_epsilon: 0.112 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 + +# Taken from SB Zoo +nas_config: + activation: relu + hidden_size: 512 diff --git a/examples/configs/incumbent/dqn_door_key_my_optimizer.yaml b/examples/configs/incumbent/dqn_door_key_my_optimizer.yaml new file mode 100644 index 000000000..afa9debff --- /dev/null +++ b/examples/configs/incumbent/dqn_door_key_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: xland_door_key + - override /algorithm: dqn + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 64 + buffer_size: 100000 + buffer_prio_sampling: false + initial_epsilon: 0.64 + target_epsilon: 0.112 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 diff --git a/examples/configs/incumbent/dqn_four_rooms_my_optimizer.yaml b/examples/configs/incumbent/dqn_four_rooms_my_optimizer.yaml new file mode 100644 index 000000000..6e7ddc29b --- /dev/null +++ b/examples/configs/incumbent/dqn_four_rooms_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: xland_four_rooms + - override /algorithm: dqn + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 64 + buffer_size: 100000 + buffer_prio_sampling: false + initial_epsilon: 0.64 + target_epsilon: 0.112 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 diff --git a/examples/configs/incumbent/ppo_battle_zone_my_optimizer.yaml b/examples/configs/incumbent/ppo_battle_zone_my_optimizer.yaml new file mode 100644 index 000000000..5270837aa --- /dev/null +++ b/examples/configs/incumbent/ppo_battle_zone_my_optimizer.yaml @@ -0,0 +1,24 @@ +# @package _global_ +defaults: + - override /environment: atari_battle_zone + - override /algorithm: ppo + +hpo_method: my_optimizer + +hp_config: + clip_eps: 0.177 + ent_coef: 0.01 + gae_lambda: 0.93 + gamma: 0.99876 + learning_rate: 0.000322 + max_grad_norm: 0.881 + minibatch_size: 64 + normalize_advantage: true + normalize_observations: false + vf_clip_eps: 0.211 + vf_coef: 0.5245 + +# Taken from SB Zoo +nas_config: + activation: relu + hidden_size: 512 diff --git a/examples/configs/incumbent/ppo_empty_random_my_optimizer.yaml b/examples/configs/incumbent/ppo_empty_random_my_optimizer.yaml new file mode 100644 index 000000000..1167c06c7 --- /dev/null +++ b/examples/configs/incumbent/ppo_empty_random_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: xland_empty_random + - override /algorithm: ppo + +hpo_method: my_optimizer + +hp_config: + clip_eps: 0.177 + ent_coef: 0.01 + gae_lambda: 0.93 + gamma: 0.99876 + learning_rate: 0.000322 + max_grad_norm: 0.881 + minibatch_size: 64 + normalize_advantage: true + normalize_observations: false + vf_clip_eps: 0.211 + vf_coef: 0.5245 diff --git a/examples/configs/incumbent/ppo_four_rooms_my_optimizer.yaml b/examples/configs/incumbent/ppo_four_rooms_my_optimizer.yaml new file mode 100644 index 000000000..49939b9d2 --- /dev/null +++ b/examples/configs/incumbent/ppo_four_rooms_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: xland_four_rooms + - override /algorithm: ppo + +hpo_method: my_optimizer + +hp_config: + clip_eps: 0.177 + ent_coef: 0.01 + gae_lambda: 0.93 + gamma: 0.99876 + learning_rate: 0.000322 + max_grad_norm: 0.881 + minibatch_size: 64 + normalize_advantage: true + normalize_observations: false + vf_clip_eps: 0.211 + vf_coef: 0.5245 diff --git a/examples/configs/incumbent/ppo_halfcheetah_my_optimizer.yaml b/examples/configs/incumbent/ppo_halfcheetah_my_optimizer.yaml new file mode 100644 index 000000000..362003a2e --- /dev/null +++ b/examples/configs/incumbent/ppo_halfcheetah_my_optimizer.yaml @@ -0,0 +1,23 @@ +# @package _global_ +defaults: + - override /environment: brax_halfcheetah + - override /algorithm: ppo + +hpo_method: my_optimizer + +hp_config: + clip_eps: 0.177 + ent_coef: 0.01 + gae_lambda: 0.93 + gamma: 0.99876 + learning_rate: 0.000322 + max_grad_norm: 0.881 + minibatch_size: 64 + normalize_advantage: true + normalize_observations: false + vf_clip_eps: 0.211 + vf_coef: 0.5245 + +# Taken from SB Zoo +nas_config: + activation: relu diff --git a/examples/configs/incumbent/ppo_lunar_lander_my_optimizer.yaml b/examples/configs/incumbent/ppo_lunar_lander_my_optimizer.yaml new file mode 100644 index 000000000..37b6bdc98 --- /dev/null +++ b/examples/configs/incumbent/ppo_lunar_lander_my_optimizer.yaml @@ -0,0 +1,19 @@ +# @package _global_ +defaults: + - override /environment: box2d_lunar_lander + - override /algorithm: ppo + +hpo_method: my_optimizer + +hp_config: + clip_eps: 0.177 + ent_coef: 0.01 + gae_lambda: 0.93 + gamma: 0.99876 + learning_rate: 0.000322 + max_grad_norm: 0.881 + minibatch_size: 64 + normalize_advantage: true + normalize_observations: false + vf_clip_eps: 0.211 + vf_coef: 0.5245 diff --git a/examples/configs/incumbent/sac_bipedal_walker_my_optimizer.yaml b/examples/configs/incumbent/sac_bipedal_walker_my_optimizer.yaml new file mode 100644 index 000000000..a428b2349 --- /dev/null +++ b/examples/configs/incumbent/sac_bipedal_walker_my_optimizer.yaml @@ -0,0 +1,18 @@ +# @package _global_ +defaults: + - override /environment: box2d_bipedal_walker + - override /algorithm: sac + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 256 + buffer_size: 100000 + buffer_prio_sampling: false + gamma: 0.99 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 + tau: 0.52 + reward_scale: 2.32 diff --git a/examples/configs/incumbent/sac_continuous_mountain_car_my_optimizer.yaml b/examples/configs/incumbent/sac_continuous_mountain_car_my_optimizer.yaml new file mode 100644 index 000000000..9e0bc8075 --- /dev/null +++ b/examples/configs/incumbent/sac_continuous_mountain_car_my_optimizer.yaml @@ -0,0 +1,18 @@ +# @package _global_ +defaults: + - override /environment: cc_continuous_mountain_car + - override /algorithm: sac + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 256 + buffer_size: 100000 + buffer_prio_sampling: false + gamma: 0.99 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 + tau: 0.52 + reward_scale: 2.32 diff --git a/examples/configs/incumbent/sac_halfcheetah_my_optimizer.yaml b/examples/configs/incumbent/sac_halfcheetah_my_optimizer.yaml new file mode 100644 index 000000000..88cb40560 --- /dev/null +++ b/examples/configs/incumbent/sac_halfcheetah_my_optimizer.yaml @@ -0,0 +1,23 @@ +# @package _global_ +defaults: + - override /environment: brax_halfcheetah + - override /algorithm: sac + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 256 + buffer_size: 100000 + buffer_prio_sampling: false + gamma: 0.99 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 + tau: 0.52 + reward_scale: 2.32 + +# Taken from SB Zoo +nas_config: + activation: relu + hidden_size: 256 diff --git a/examples/configs/incumbent/sac_pendulum_my_optimizer.yaml b/examples/configs/incumbent/sac_pendulum_my_optimizer.yaml new file mode 100644 index 000000000..2a612feb1 --- /dev/null +++ b/examples/configs/incumbent/sac_pendulum_my_optimizer.yaml @@ -0,0 +1,18 @@ +# @package _global_ +defaults: + - override /environment: cc_pendulum + - override /algorithm: sac + +hpo_method: my_optimizer + +hp_config: + buffer_batch_size: 256 + buffer_size: 100000 + buffer_prio_sampling: false + gamma: 0.99 + learning_rate: 0.0023 + learning_starts: 1032 + use_target_network: true + target_update_interval: 10 + tau: 0.52 + reward_scale: 2.32 diff --git a/examples/evaluation/default_method/dqn_CartPole-v1/100/multirun.yaml b/examples/evaluation/default_method/dqn_CartPole-v1/100/multirun.yaml new file mode 100644 index 000000000..a1d876e54 --- /dev/null +++ b/examples/evaluation/default_method/dqn_CartPole-v1/100/multirun.yaml @@ -0,0 +1,216 @@ +hydra: + run: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + sweep: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: MULTIRUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=MULTIRUN + task: + - autorl.seed=100 + - +incumbent=glob(*) + job: + name: run_arlbench + chdir: true + override_dirname: +incumbent=glob(*),autorl.seed=100 + id: ??? + num: ??? + config_name: evaluate + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.3' + cwd: /Users/jannisbecktepe/Developer/arlbench_main/examples + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /Users/jannisbecktepe/Developer/arlbench_main/examples/configs + schema: file + provider: main + - path: hydra_plugins.hydra_colorlog.conf + schema: pkg + provider: hydra-colorlog + - path: '' + schema: structured + provider: schema + output_dir: ??? + choices: + environment: cc_cartpole + algorithm: dqn + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false +jax_enable_x64: true +load_checkpoint: '' +hpo_method: default_method +autorl: + seed: 100 + env_framework: ${environment.framework} + env_name: ${environment.name} + env_kwargs: ${environment.kwargs} + eval_env_kwargs: ${environment.eval_kwargs} + n_envs: ${environment.n_envs} + algorithm: ${algorithm} + cnn_policy: ${environment.cnn_policy} + nas_config: ${nas_config} + n_total_timesteps: ${environment.n_total_timesteps} + checkpoint: [] + checkpoint_name: default_checkpoint + checkpoint_dir: /tmp + state_features: [] + objectives: + - reward_mean + optimize_objectives: upper + n_steps: 1 + n_eval_steps: 100 + n_eval_episodes: 10 +algorithm: dqn +hp_config: + buffer_prio_sampling: false + buffer_alpha: 0.9 + buffer_beta: 0.9 + buffer_epsilon: 0.001 + buffer_batch_size: 16 + buffer_size: 1000000 + initial_epsilon: 1.0 + target_epsilon: 0.05 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0003 + learning_starts: 128 + normalize_observations: false + train_freq: 4 + use_target_network: true + target_update_interval: 1000 + tau: 1.0 +nas_config: + activation: tanh + hidden_size: 64 +environment: + name: CartPole-v1 + framework: gymnax + n_total_timesteps: 100000.0 + kwargs: {} + eval_kwargs: {} + cnn_policy: false + deterministic_eval: true + n_envs: 8 diff --git a/examples/evaluation/my_optimizer/dqn_Acrobot-v1/100/multirun.yaml b/examples/evaluation/my_optimizer/dqn_Acrobot-v1/100/multirun.yaml new file mode 100644 index 000000000..ad9b68413 --- /dev/null +++ b/examples/evaluation/my_optimizer/dqn_Acrobot-v1/100/multirun.yaml @@ -0,0 +1,217 @@ +hydra: + run: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + sweep: + dir: evaluation/${hpo_method}/${algorithm}_${environment.name}/${autorl.seed} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: MULTIRUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=MULTIRUN + task: + - autorl.seed=100 + - incumbent=glob(*) + job: + name: run_arlbench + chdir: true + override_dirname: autorl.seed=100,incumbent=glob(*) + id: ??? + num: ??? + config_name: evaluate + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.3' + cwd: /Users/jannisbecktepe/Developer/arlbench_main/examples + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /Users/jannisbecktepe/Developer/arlbench_main/examples/configs + schema: file + provider: main + - path: hydra_plugins.hydra_colorlog.conf + schema: pkg + provider: hydra-colorlog + - path: '' + schema: structured + provider: schema + output_dir: ??? + choices: + incumbent: dqn_acrobot_my_optimizer + environment: cc_acrobot + algorithm: dqn + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false +jax_enable_x64: true +load_checkpoint: '' +hpo_method: my_optimizer +autorl: + seed: 100 + env_framework: ${environment.framework} + env_name: ${environment.name} + env_kwargs: ${environment.kwargs} + eval_env_kwargs: ${environment.eval_kwargs} + n_envs: ${environment.n_envs} + algorithm: ${algorithm} + cnn_policy: ${environment.cnn_policy} + nas_config: ${nas_config} + n_total_timesteps: ${environment.n_total_timesteps} + checkpoint: [] + checkpoint_name: default_checkpoint + checkpoint_dir: /tmp + state_features: [] + objectives: + - reward_mean + optimize_objectives: upper + n_steps: 1 + n_eval_steps: 100 + n_eval_episodes: 10 +algorithm: dqn +hp_config: + buffer_prio_sampling: false + buffer_alpha: 0.9 + buffer_beta: 0.9 + buffer_epsilon: 0.001 + buffer_batch_size: 64 + buffer_size: 100000 + initial_epsilon: 0.64 + target_epsilon: 0.112 + gamma: 0.99 + gradient_steps: 1 + learning_rate: 0.0023 + learning_starts: 1032 + normalize_observations: false + train_freq: 4 + use_target_network: true + target_update_interval: 10 + tau: 1.0 +nas_config: + activation: tanh + hidden_size: 64 +environment: + name: Acrobot-v1 + framework: gymnax + n_total_timesteps: 100000.0 + kwargs: {} + eval_kwargs: {} + cnn_policy: false + deterministic_eval: true + n_envs: 8 diff --git a/examples/run_arlbench.py b/examples/run_arlbench.py index 6ceee66a8..3f37f4bc4 100644 --- a/examples/run_arlbench.py +++ b/examples/run_arlbench.py @@ -5,7 +5,6 @@ import warnings warnings.filterwarnings("ignore") -import csv import logging import sys import traceback @@ -40,18 +39,6 @@ def execute(cfg: DictConfig): def run(cfg: DictConfig, logger: logging.Logger): """Console script for arlbench.""" - # check if file done exists and if so, return - try: - with open("./done.txt") as f: - logger.info("Job already done, returning.") - - with open("./performance.csv") as pf: - csvreader = csv.reader(pf) - performance = next(csvreader) - return float(performance[0]) - except FileNotFoundError: - pass - objectives = run_arlbench(cfg, logger=logger) with open("./performance.csv", "w+") as f: diff --git a/examples/run_heuristic_schedule.py b/examples/run_heuristic_schedule.py index f3023e870..2e09bec0b 100644 --- a/examples/run_heuristic_schedule.py +++ b/examples/run_heuristic_schedule.py @@ -5,11 +5,11 @@ import warnings warnings.filterwarnings("ignore") +import json import logging import sys import traceback from typing import TYPE_CHECKING -import json import hydra import jax diff --git a/examples/run_reactive_schedule.py b/examples/run_reactive_schedule.py index 63792ed2c..a1a4a9510 100644 --- a/examples/run_reactive_schedule.py +++ b/examples/run_reactive_schedule.py @@ -5,11 +5,11 @@ import warnings warnings.filterwarnings("ignore") +import json import logging import sys import traceback from typing import TYPE_CHECKING -import json import hydra import jax @@ -35,7 +35,7 @@ def run(cfg: DictConfig, logger: logging.Logger): tolerance = 1e-4 rewards = [] lrs = [] - for i in range(100): + for _i in range(100): lrs.append(cfg.hp_config.learning_rate) # Statistics here contain the number of steps and gradient information statistics, objectives, te, tr, _ = env.step(cfg.hp_config) diff --git a/run_arlbench.py b/run_arlbench.py index b0801b1a4..c6bc40fef 100644 --- a/run_arlbench.py +++ b/run_arlbench.py @@ -5,7 +5,6 @@ import warnings warnings.filterwarnings("ignore") -import csv import logging import sys import traceback @@ -40,18 +39,6 @@ def execute(cfg: DictConfig): def run(cfg: DictConfig, logger: logging.Logger): """Console script for arlbench.""" - # check if file done exists and if so, return - try: - with open("./done.txt") as f: - logger.info("Job already done, returning.") - - with open("./performance.csv") as pf: - csvreader = csv.reader(pf) - performance = next(csvreader) - return float(performance[0]) - except FileNotFoundError: - pass - objectives = run_arlbench(cfg, logger=logger) logger.info(f"Returned objectives: {objectives}") diff --git a/tests/autorl/test_autorl_env.py b/tests/autorl/test_autorl_env.py index 64146234b..4b3bc9638 100644 --- a/tests/autorl/test_autorl_env.py +++ b/tests/autorl/test_autorl_env.py @@ -1,7 +1,6 @@ from __future__ import annotations import pytest - from arlbench import AutoRLEnv from arlbench.core.algorithms import DQN diff --git a/tests/autorl/test_checkpointing.py b/tests/autorl/test_checkpointing.py index ef2f44605..778d0f395 100644 --- a/tests/autorl/test_checkpointing.py +++ b/tests/autorl/test_checkpointing.py @@ -2,7 +2,6 @@ import jax import numpy as np - from arlbench.autorl import AutoRLEnv from arlbench.autorl.checkpointing import Checkpointer from arlbench.core.algorithms import DQN @@ -119,7 +118,7 @@ def test_checkpointing_ppo(): env._load(checkpoint_path=info["checkpoint"], seed=42) env.step(action, n_total_timesteps=10, n_eval_episodes=10, n_eval_steps=1) - + new_reward = np.mean(env.eval(10)) assert np.isclose(reward, new_reward, rtol=1.0) @@ -147,7 +146,7 @@ def test_checkpointing_sac(): env._load(checkpoint_path=info["checkpoint"], seed=42) env.step(action, n_total_timesteps=10, n_eval_episodes=10, n_eval_steps=1) - + new_reward = np.mean(env.eval(10)) assert np.isclose(reward, new_reward, rtol=1.0) diff --git a/tests/autorl/test_objectives.py b/tests/autorl/test_objectives.py index 8dad53ab1..716e00587 100644 --- a/tests/autorl/test_objectives.py +++ b/tests/autorl/test_objectives.py @@ -1,7 +1,6 @@ from __future__ import annotations import numpy as np - from arlbench import AutoRLEnv from arlbench.core.algorithms import DQN @@ -102,7 +101,7 @@ def test_autorl_env_dqn_multi_objective(): reward = np.mean(rewards) assert np.abs(objectives["reward_mean"] - reward) < 5 - assert objectives["runtime"] < 0 + assert objectives["runtime"] < 0 assert objectives["emissions"] < 0 diff --git a/tests/core/algorithms/test_dqn.py b/tests/core/algorithms/test_dqn.py index 8959be50a..4f4e5eebe 100644 --- a/tests/core/algorithms/test_dqn.py +++ b/tests/core/algorithms/test_dqn.py @@ -3,7 +3,6 @@ import time import jax - from arlbench.core.algorithms import DQN from arlbench.core.environments import make_env diff --git a/tests/core/algorithms/test_ppo.py b/tests/core/algorithms/test_ppo.py index 06160b044..82d075358 100644 --- a/tests/core/algorithms/test_ppo.py +++ b/tests/core/algorithms/test_ppo.py @@ -4,7 +4,6 @@ import warnings import jax - from arlbench.core.algorithms import PPO from arlbench.core.environments import make_env diff --git a/tests/core/algorithms/test_sac.py b/tests/core/algorithms/test_sac.py index efe951706..d109a0f61 100644 --- a/tests/core/algorithms/test_sac.py +++ b/tests/core/algorithms/test_sac.py @@ -1,10 +1,8 @@ from __future__ import annotations import time -import warnings import jax - from arlbench.core.algorithms import SAC from arlbench.core.environments import make_env diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py index de5396da7..08ad37b8a 100644 --- a/tests/utils/test_utils.py +++ b/tests/utils/test_utils.py @@ -2,9 +2,8 @@ import gymnasium import numpy as np -from ConfigSpace import Categorical, ConfigurationSpace, Float, Integer - from arlbench.utils import config_space_to_gymnasium_space +from ConfigSpace import Categorical, ConfigurationSpace, Float, Integer def test_config_space_to_gymnasium_space():

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