| ---
|
| tags:
|
| - Taxi-v3
|
| - q-learning
|
| - reinforcement-learning
|
| - custom-implementation
|
| model-index:
|
| - name: q-Taxi-v3
|
| results:
|
| - metrics:
|
| - type: mean_reward
|
| value: 7.56 +/- 2.71
|
| name: mean_reward
|
| task:
|
| type: reinforcement-learning
|
| name: reinforcement-learning
|
| dataset:
|
| name: Taxi-v3
|
| type: Taxi-v3
|
| ---
|
|
|
| # **Q-Learning** Agent playing **Taxi-v3**
|
| This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
|
|
|
| ## Usage
|
| ```python
|
| model = load_from_hub(repo_id="btsas/q-Taxi-v3", filename="q-learning.pkl")
|
|
|
| # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
|
| env = gym.make(model["env_id"])
|
|
|
| evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
|
|
|
| ```
|
| |
