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