README.md

metadata

tags:
  - Taxi-v3
  - q-learning
  - reinforcement-learning
  - custom-implementation
model-index:
  - name: taxi-v3
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: Taxi-v3
          type: Taxi-v3
        metrics:
          - type: mean_reward
            value: 7.58 +/- 2.72
            name: mean_reward
            verified: false

Q-Learning Agent playing1 Taxi-v3

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

Usage

1. Load model

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

2. Evaluate model

model = load_from_hub(repo_id="thien1892/q-taxi-v3", filename="q-learning.pkl") # Try to use another model print(model) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])