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README.md

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+---
+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).

q-learning.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c10eaf619e18aed727527b1b22f82cc71445fd355946b7d470400b1f2783be1
+size 24572

results.json

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+{"env_id": "Taxi-v3", "mean_reward": 7.56, "n_eval_episodes": 100, "eval_datetime": "2025-02-19T15:43:22.619288"}