Training Minigrid Environments#

The environments in the Minigrid library can be trained easily using StableBaselines3. In this tutorial we show how a PPO agent can be trained on the MiniGrid-Empty-16x16-v0 environment.

Create Custom Feature Extractor#

Although StableBaselines3 is fully compatible with Gymnasium-based environments (which includes Minigrid), the default CNN architecture does not directly support the Minigrid observation space. Thus, to train an agent on Minigrid environments, we need to create a custom feature extractor. This can be done by creating a feature extractor class that inherits from stable_baselines3.common.torch_layers.BaseFeaturesExtractor

class MinigridFeaturesExtractor(BaseFeaturesExtractor):
    def __init__(self, observation_space: gym.Space, features_dim: int = 512, normalized_image: bool = False) -> None:
        super().__init__(observation_space, features_dim)
        n_input_channels = observation_space.shape[0]
        self.cnn = nn.Sequential(
            nn.Conv2d(n_input_channels, 16, (2, 2)),
            nn.ReLU(),
            nn.Conv2d(16, 32, (2, 2)),
            nn.ReLU(),
            nn.Conv2d(32, 64, (2, 2)),
            nn.ReLU(),
            nn.Flatten(),
        )

        # Compute shape by doing one forward pass
        with torch.no_grad():
            n_flatten = self.cnn(torch.as_tensor(observation_space.sample()[None]).float()).shape[1]

        self.linear = nn.Sequential(nn.Linear(n_flatten, features_dim), nn.ReLU())

    def forward(self, observations: torch.Tensor) -> torch.Tensor:
        return self.linear(self.cnn(observations))

This class is created based on the custom feature extractor documentation, the CNN architecture is copied from Lucas Willems’ rl-starter-files.

Train a PPO Agent#

The using the custom feature extractor, we can train a PPO agent on the MiniGrid-Empty-16x16-v0 environment. The following code snippet shows how this can be done.

import minigrid
from minigrid.wrappers import ImgObsWrapper
from stable_baselines3 import PPO

policy_kwargs = dict(
    features_extractor_class=MinigridFeaturesExtractor,
    features_extractor_kwargs=dict(features_dim=128),
)

env = gym.make("MiniGrid-Empty-16x16-v0", render_mode="rgb_array")
env = ImgObsWrapper(env)

model = PPO("CnnPolicy", env, policy_kwargs=policy_kwargs, verbose=1)
model.learn(2e5)

By default the observation of Minigrid environments are dictionaries. Since the CnnPolicy from StableBaseline3 by default takes in image observations, we need to wrap the environment using the ImgObsWrapper from the Minigrid library. This wrapper converts the dictionary observation to an image observation.

Training Minigrid Environments#

Create Custom Feature Extractor#

Train a PPO Agent#

Further Reading#