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--- |
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tags: |
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- Taxi-v3 |
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- q-learning |
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- reinforcement-learning |
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- custom-implementation |
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model-index: |
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- name: q-taxi-v3 |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: Taxi-v3 |
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type: Taxi-v3 |
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metrics: |
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- type: mean_reward |
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value: 13.36 +/- 2.11 |
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name: mean_reward |
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verified: false |
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--- |
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# **Q-Learning** Agent playing1 **Taxi-v3** |
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This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . |
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## Codes |
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Github repos(Give a star if found useful): |
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* https://github.com/hishamcse/DRL-Renegades-Game-Bots |
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* https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots |
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* https://github.com/hishamcse/Robo-Chess |
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Kaggle Notebook: |
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* https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-2-frozenlake-v1-taxi-v3 |
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## Usage |
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```python |
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model = load_from_hub(repo_id="hishamcse/q-taxi-v3", filename="q-learning.pkl") |
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# Don't forget to check if you need to add additional attributes (is_slippery=False etc) |
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env = gym.make(model["env_id"]) |
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``` |
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