add task customization instructions

docs/source/tutorials.rst

@@ -185,3 +185,83 @@ Load MyoReflex Baseline
 
 To load and execute the MyoReflex controller with baseline parameters.
 Run the MyoReflex tutorial `here <https://github.com/facebookresearch/myosuite/tree/main/docs/source/tutorials/4b_reflex>`__
+
+
+
+Customizing Tasks
+=================
+
+In order to create a new customized task, there are two places where you need to act:
+
+1. Set up a new environment class for the new task
+
+2. Register the new task
+
+Set up a new environment
++++++++++++++++++++++++++
+
+Environment classes are developed according to the OpenAI Gym definition
+and contain all the information specific for a task,
+to interact with the environment, to observe it and to
+act on it. In addition, each environment class contains
+a reward function which converts the observation into a
+number that establishes how good the observation is with
+respect to the task objectives. In order to create a new
+task, a new environment class needs to be generated eg.
+reach2_v0.py (see for example how `reach_v0.py <https://github.com/MyoHub/myosuite/blob/main/myosuite/envs/myo/myobase/reach_v0.py>`__ is structured).
+In this file, it is possible to specify the type of observation (eg. joint angles, velocities, forces), actions (e.g. muscle, motors), goal, and reward.
+
+
+.. code-block:: python
+
+    from myosuite.envs.myo.base_v0 import BaseV0
+    import deprl
+
+    # Class extends Basev0
+    class NewReachEnvV0(BaseV0):
+        ....
+
+    # defines the observation
+    def get_obs_dict(self, sim):
+        ....
+
+    # defines the rewards
+    def get_reward_dict(self, obs_dict):
+        ...
+
+    #reset condition that
+    def reset(self):
+        ...
+
+    # step the simulation forward by acting on the environment
+    def step(self, a, **kwargs):
+       ...
+       return observation, reward, task_terminated, environment_information
+
+.. _setup_base_class:
+
+
+Register the new environment
+++++++++++++++++++++++++++++++
+
+Once defined the task `reach2_v0.py`, the new environment needs to be registered to be
+visible when importing `myosuite`. This is achieved by introducing the new environment in
+the `__init__.py` (called when the library is imported) where the registration routine happens.
+The registration of the new enviornment is obtained adding:
+
+.. code-block:: python
+
+   register_env_with_variants(id='newReachTask-v0',
+        entry_point='myosuite.envs.myo.myobase.reach_v0:NewReachEnvV0', # where to find the new Environment Class
+        max_episode_steps=200, # duration of the episode
+        kwargs={
+            'model_path': curr_dir+'/../assets/hand/myohand_pose.xml', # where the xml file of the environment is located
+            'target_reach_range': {'IFtip': ((0.1, 0.05, 0.20), (0.2, 0.05, 0.20)),}, # this is used in the setup to define the goal e.g. rando position of the team between 0.1 and 0.2 in the x coordinates
+            'normalize_act': True, # if to use normalized actions using a sigmoid function.
+            'frame_skip': 5, # collect a sample every 5 iteration step
+        }
+    )
+
+
+.. _register_new_environment:
+