---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: drl-course-unit-02-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

  ```python
import gymnasium as gym
import pickle5 as pickle
from huggingface_sb3 import load_from_hub
from hf_course_code import evaluate_agent # Code from the course https://huggingface.co/learn/deep-rl-course/unit2/hands-on#the-evaluation-method-

model_pickle = load_from_hub(repo_id="jostyposty/drl-course-unit-02-taxi-v3", filename="q-learning.pkl")

with open(model_pickle, "rb") as f:
    model = pickle.load(f)

env = gym.make(model["env_id"])

mean_reward, std_reward = evaluate_agent(
    env,
    model["max_steps"],
    model["n_eval_episodes"],
    model["qtable"],
    model["eval_seed"],
)
result = mean_reward - std_reward
print(f"Result={result:.2f}, Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}")
  ```