.. _custom_env:

Using Custom Environments
==========================

To use the RL baselines with custom environments, they just need to follow the *gymnasium*  `interface <https://gymnasium.farama.org/tutorials/gymnasium_basics/environment_creation/#sphx-glr-tutorials-gymnasium-basics-environment-creation-py>`_.
That is to say, your environment must implement the following methods (and inherits from Gym Class):


.. note::

  If you are using images as input, the observation must be of type ``np.uint8`` and be contained in [0, 255].
  By default, the observation is normalized by SB3 pre-processing (dividing by 255 to have values in [0, 1]) when using CNN policies.
  Images can be either channel-first or channel-last.

  If you want to use ``CnnPolicy`` or ``MultiInputPolicy`` with image-like observation (3D tensor) that are already normalized, you must pass ``normalize_images=False``
  to the policy (using ``policy_kwargs`` parameter, ``policy_kwargs=dict(normalize_images=False)``)
  and make sure your image is in the **channel-first** format.


.. note::

  Although SB3 supports both channel-last and channel-first images as input, we recommend using the channel-first convention when possible.
  Under the hood, when a channel-last image is passed, SB3 uses a ``VecTransposeImage`` wrapper to re-order the channels.



.. code-block:: python

  import gymnasium as gym
  import numpy as np
  from gymnasium import spaces


  class CustomEnv(gym.Env):
      """Custom Environment that follows gym interface."""

      metadata = {"render_modes": ["human"], "render_fps": 30}

      def __init__(self, arg1, arg2, ...):
          super().__init__()
          # Define action and observation space
          # They must be gym.spaces objects
          # Example when using discrete actions:
          self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)
          # Example for using image as input (channel-first; channel-last also works):
          self.observation_space = spaces.Box(low=0, high=255,
                                              shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)

      def step(self, action):
          ...
          return observation, reward, terminated, truncated, info

      def reset(self, seed=None, options=None):
          ...
          return observation, info

      def render(self):
          ...

      def close(self):
          ...


Then you can define and train a RL agent with:

.. code-block:: python

  # Instantiate the env
  env = CustomEnv(arg1, ...)
  # Define and Train the agent
  model = A2C("CnnPolicy", env).learn(total_timesteps=1000)


To check that your environment follows the Gym interface that SB3 supports, please use:

.. code-block:: python

	from stable_baselines3.common.env_checker import check_env

	env = CustomEnv(arg1, ...)
	# It will check your custom environment and output additional warnings if needed
	check_env(env)

Gymnasium also have its own `env checker <https://gymnasium.farama.org/api/utils/#gymnasium.utils.env_checker.check_env>`_ but it checks a superset of what SB3 supports (SB3 does not support all Gym features).

We have created a `colab notebook <https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/sb3/5_custom_gym_env.ipynb>`_ for a concrete example on creating a custom environment along with an example of using it with Stable-Baselines3 interface.

Alternatively, you may look at Gymnasium `built-in environments <https://gymnasium.farama.org>`_.

Optionally, you can also register the environment with gym, that will allow you to create the RL agent in one line (and use ``gym.make()`` to instantiate the env):

.. code-block:: python

	from gymnasium.envs.registration import register
	# Example for the CartPole environment
	register(
	    # unique identifier for the env `name-version`
	    id="CartPole-v1",
	    # path to the class for creating the env
	    # Note: entry_point also accept a class as input (and not only a string)
	    entry_point="gym.envs.classic_control:CartPoleEnv",
	    # Max number of steps per episode, using a `TimeLimitWrapper`
	    max_episode_steps=500,
	)



In the project, for testing purposes, we use a custom environment named ``IdentityEnv``
defined `in this file <https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/common/envs/identity_env.py>`_.
An example of how to use it can be found `here <https://github.com/DLR-RM/stable-baselines3/blob/master/tests/test_identity.py>`_.
