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: 7.56 +/- 2.71
            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

import gymnasium as gym
from huggingface_sb3 import load_from_hub
import numpy as np
import pickle

# Load the model
env_name = "Taxi-v3"
model_name = "q-Taxi-v3"
model_path = load_from_hub(repo_id="ch-bz/" + model_name, filename="q-learning.pkl")
Qtable = pickle.load(open(model_path, "rb"))["qtable"]
env = gym.make("Taxi-v3", render_mode="human")
state, info = env.reset()

while True:
    action = np.argmax(Qtable[state][:])
    state, reward, terminated, truncated, info = env.step(action)
    env.render()
    
    if terminated or truncated:
        state, info = env.reset()