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How can I integrate a custom environment with RL_Coach using OpenAI Gym


To integrate a custom environment with RL_Coach using OpenAI Gym, you can follow these steps:

Step 1: Implement the Environment

1. Create a New Environment Class:
- Create a new Python file for your environment and define a class that inherits from `gym.Env`. This class should implement the necessary methods for the environment, such as `reset`, `step`, `render`, and `close`.

2. Register the Environment:
- Register your environment using `gym.envs.registration.register`. This allows you to create instances of your environment using `make`.

Step 2: Configure the Environment in RL_Coach

1. Create a New Environment Parameters Class:
- Create a new class that inherits from `rl_coach.environments.environment_parameters.EnvironmentParameters`. This class should define the parameters for your environment.

2. Configure the Environment in the Preset:
- In your preset file, define the environment parameters using the `GymEnvironmentParameters` class. Specify the path to your environment source code using the `level` parameter.

Example Code

Here is an example of how you can integrate a custom environment with RL_Coach using OpenAI Gym:

python
# myenv.py
import gym
from gym import spaces

class MyEnvironment(gym.Env):
    def __init__(self):
        self.observation_space = spaces.Box(low=0, high=1, shape=(4,))
        self.action_space = spaces.Discrete(2)

    def reset(self):
        # Implement your environment logic here
        pass

    def step(self, action):
        # Implement your environment logic here
        pass

    def render(self, mode='human'):
        # Implement your environment logic here
        pass

    def close(self):
        # Implement your environment logic here
        pass

# myenv_presets.py
from rl_coach.environments.gym_environment import GymEnvironmentParameters

class MyEnvironmentParameters(GymEnvironmentParameters):
    def __init__(self):
        super().__init__()
        self.level = "myenv:MyEnvironment"
        self.additional_simulator_parameters = {"time_limit": 1000}

# preset.py
from rl_coach.preset import Preset
from myenv_presets import MyEnvironmentParameters

class MyPreset(Preset):
    def __init__(self):
        super().__init__()
        self.graph_manager = GraphManager()
        self.environment_parameters = MyEnvironmentParameters()

# Usage
preset = MyPreset()
agent = rl_coach.agents.dqn_agent.DQNAgent(preset)
agent.train(env, 1000)

Additional Tips

- Use Existing Environments: If your environment is already compliant with the OpenAI Gym interface, you can use it directly in RL_Coach without any additional setup.
- Custom Visualization: You can create custom visualizations for your environment by implementing the `render` method in your environment class.
- Custom Agent: You can create custom agents by implementing the necessary methods in the agent class. For example, you can implement a custom Q-value computation in the `compute_q_values` method.

By following these steps and tips, you can effectively integrate your custom environment with RL_Coach using OpenAI Gym.

Citations:
[1] https://openrl-docs.readthedocs.io/en/latest/custom_env/index.html
[2] https://github.com/NickKaparinos/OpenAI-Gym-Projects
[3] https://github.com/NervanaSystems/coach/issues/383
[4] https://intellabs.github.io/coach/contributing/add_env.html
[5] https://stackoverflow.com/questions/59024640/rl-coach-how-to-use-custom-presets