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---
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}")
```
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