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--- |
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tags: |
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- Taxi-v3 |
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- q-learning |
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- reinforcement-learning |
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- custom-implementation |
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model-index: |
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- name: q-Taxi-v1-5x5 |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: Taxi-v3 |
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type: Taxi-v3 |
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metrics: |
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- type: mean_reward |
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value: 7.36 +/- 2.47 |
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name: mean_reward |
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verified: false |
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--- |
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# **Q-Learning** Agent playing1 **Taxi-v3** |
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This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . |
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## Usage |
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```python |
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from huggingface_sb3 import load_from_hub |
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import gymnasium as gym |
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from tqdm import tqdm |
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import numpy as np |
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import pickle |
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def greedy_policy(Qtable, state): |
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action = np.argmax(Qtable[state, :]) |
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return action |
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def evaluate_agent(env: gym.Env, max_steps: int, n_eval_episodes: int, Q: np.ndarray, seed: list[int]): |
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""" |
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Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward. |
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:param env: The evaluation environment |
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:param max_steps: Maximum number of steps per episode |
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:param n_eval_episodes: Number of episode to evaluate the agent |
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:param Q: The Q-table |
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:param seed: The evaluation seed array (for taxi-v3) |
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""" |
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episode_rewards = [] |
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for episode in tqdm(range(n_eval_episodes)): |
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if seed: |
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state, info = env.reset(seed=seed[episode]) |
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else: |
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state, info = env.reset() |
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truncated = False |
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terminated = False |
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total_rewards_ep = 0 |
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for step in range(max_steps): |
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action = greedy_policy(Q, state) |
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new_state, reward, terminated, truncated, info = env.step(action) |
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total_rewards_ep += reward |
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if terminated or truncated: |
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break |
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state = new_state |
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episode_rewards.append(total_rewards_ep) |
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mean_reward = np.mean(episode_rewards) |
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std_reward = np.std(episode_rewards) |
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return float(mean_reward), float(std_reward) |
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if __name__ == "__main__": |
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file_path = load_from_hub(repo_id="BobChuang/q-Taxi-v1-5x5", filename="q-learning.pkl") |
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with open(file_path, "rb") as f: |
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model = pickle.load(f) |
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env = gym.make(model["env_id"], render_mode="rgb_array") |
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max_steps = model["max_steps"] |
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n_eval_episodes = model["n_eval_episodes"] |
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qtable = model["qtable"] |
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eval_seed = model["eval_seed"] |
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mean_reward, std_reward = evaluate_agent(env, max_steps, n_eval_episodes, qtable, eval_seed) |
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print(f"\n{ mean_reward = }, { std_reward = }") |
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``` |
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