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] as the observation is normalized (dividing by 255 to have values in [0, 1]) when using CNN policies.

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:
self.observation_space = spaces.Box(low=0, high=255,
shape=(HEIGHT, WIDTH, N_CHANNELS), dtype=np.uint8)

def step(self, action):
...
def reset(self):
...
def render(self, mode='human', close=False):
...


Then you can define and train a RL agent with:

# Instantiate and wrap the env
env = DummyVecEnv([lambda: CustomEnv(arg1, ...)])
# Define and Train the agent
model = A2C(CnnPolicy, env).learn(total_timesteps=1000)


You can find a complete guide online 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. An example of how to use it can be found here.