Using Custom Environments

To use the RL baselines with custom environments, they just need to follow the gymnasium interface. That is to say, your environment must implement the following methods (and inherits from Gym Class):


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.


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.

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, ...):
        # 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:

# 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:

from stable_baselines3.common.env_checker import check_env

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

Gymnasium also have its own env checker but it checks a superset of what SB3 supports (SB3 does not support all Gym features).

We have created a colab notebook 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.

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):

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

In the project, for testing purposes, we use a custom environment named IdentityEnv defined in this file. An example of how to use it can be found here.