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MiniGrid

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Scores and learning cures of various RL algorithms on the MiniGrid benchmark. Environment link: https://github.com/Farama-Foundation/Minigrid Number of environments: 16 Number of training steps: 1,000,000 Number of seeds: 10 Added algorithms: [A2C]

Methods:

.load_scores

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.load_scores(
   env_id: str, agent: str
)

Returns final performance.

Args

  • env_id (str) : Environment ID.
  • agent_id (str) : Agent name.

Returns

Test scores data array with shape (N_SEEDS, N_POINTS).

.load_curves

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.load_curves(
   env_id: str, agent: str
)

Returns learning curves using a Dict of NumPy arrays.

Args

  • env_id (str) : Environment ID.
  • agent_id (str) : Agent name.

Returns

  • train : np.ndarray(shape=(N_SEEDS, N_POINTS))
  • eval : np.ndarray(shape=(N_SEEDS, N_POINTS)) Learning curves data with structure: curves

.load_models

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.load_models(
   env_id: str, agent: str, seed: int, device: str = 'cpu'
)

Load the model from the hub.

Args

  • env_id (str) : Environment ID.
  • agent (str) : Agent name.
  • seed (int) : The seed to load.
  • device (str) : The device to load the model on.

Returns

The loaded model.

.load_apis

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.load_apis(
   env_id: str, agent: str, seed: int, device: str = 'cpu'
)

Load the a training API.

Args

  • env_id (str) : Environment ID.
  • agent (str) : Agent name.
  • seed (int) : The seed to load.
  • device (str) : The device to load the model on.

Returns

The loaded API.