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Using Custom Environments
==========================
To use the rl baselines with custom environments, they just need to follow the *gym* interface.
That is to say, your environment must implement the following methods (and inherits from OpenAI Gym Class):
.. note::
If you are using images as input, the input values must be in [0, 255] and np.uint8 as the observation
is normalized (dividing by 255 to have values in [0, 1]) when using CNN policies. Images can be either
channel-first or channel-last.
.. code-block:: python
import gym
from gym import spaces
class CustomEnv(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, arg1, arg2, ...):
super(CustomEnv, self).__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 (can be channel-first or channel-last):
self.observation_space = spaces.Box(low=0, high=255,
shape=(HEIGHT, WIDTH, N_CHANNELS), dtype=np.uint8)
def step(self, action):
...
return observation, reward, done, info
def reset(self):
...
return observation # reward, done, info can't be included
def render(self, mode='human'):
...
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, 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)
We have created a `colab notebook <https://colab.research.google.com/github/araffin/rl-tutorial-jnrr19/blob/master/5_custom_gym_env.ipynb>`_ for
a concrete example of creating a custom environment.
You can also find a `complete guide online <https://github.com/openai/gym/blob/master/docs/creating-environments.md>`_
on creating a custom Gym environment.
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).
In the project, for testing purposes, we use a custom environment named ``IdentityEnv``
defined `in this file <https://github.com/hill-a/stable-baselines/blob/master/stable_baselines/common/identity_env.py>`_.
An example of how to use it can be found `here <https://github.com/hill-a/stable-baselines/blob/master/tests/test_identity.py>`_.
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