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---
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
```python
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()
```
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