| tags: | |
| - Taxi-v3 | |
| - q-learning | |
| - reinforcement-learning | |
| - custom-implementation | |
| - gymnasium | |
| 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 playing **Taxi-v3** | |
| This is a trained model of a **Q-Learning** agent playing to the Gymnasium **Taxi-v3** reinforcement learning environment. | |
| ## Usage | |
| ```python | |
| model = load_from_hub(repo_id="coding-kelps/q-taxi-v3", filename="q-learning.pkl") | |
| # Don't forget to check if you need to add additional attributes | |
| env = gym.make(model["env_id"]) | |
| ``` | |
| ## References | |
| You can find the original source code of the model training in the corresponding [Coding Kelps aquaqym repository](https://github.com/coding-kelps/aquagym). | |