README.md

metadata

tags:
  - Taxi-v3
  - q-learning
  - reinforcement-learning
  - custom-implementation
model-index:
  - name: q-Taxi-v3
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: Taxi-v3
          type: Taxi-v3
        metrics:
          - type: mean_reward
            value: 7.50 +/- 2.70
            name: mean_reward
            verified: false

Q-Learning Agent playing Taxi-v3

This is a trained model of a Q-Learning agent playing Taxi-v3 .

Usage

from urllib.error import HTTPError

from huggingface_hub import hf_hub_download


def load_from_hub(repo_id: str, filename: str) -> str:
    """
    Download a model from Hugging Face Hub.
    :param repo_id: id of the model repository from the Hugging Face Hub
    :param filename: name of the model zip file from the repository
    """
    # Get the model from the Hub, download and cache the model on your local disk
    pickle_model = hf_hub_download(repo_id=repo_id, filename=filename)

    with open(pickle_model, "rb") as f:
        downloaded_model_file = pickle.load(f)

    return downloaded_model_file

model = load_from_hub(repo_id="AdanLee/q-Taxi-v3", filename="q-learning.pkl")

Don't forget to check if you need to add additional attributes (is_slippery=False etc)

env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])