Push Q-Learning agent to Hub
Browse files- .gitattributes +1 -0
- README.md +35 -0
- q-learning.pkl +3 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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README.md
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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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- metrics:
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- type: mean_reward
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value: 7.56 +/- 2.71
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name: mean_reward
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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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---
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# **Q-Learning** Agent playing **Taxi-v3**
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This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
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## Usage
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```python
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model = load_from_hub(repo_id="Mahmoud7/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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evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
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```
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q-learning.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:82fbb99126d805b35219ad186619a31cd2232f4eb9c4a3561e9e1800a5932e55
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size 24589
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replay.mp4
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version https://git-lfs.github.com/spec/v1
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oid sha256:0bc53ec0596702d486b315c82d88acc9720576f9d6fd1fcdc0381fef528ed305
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size 119080
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results.json
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{"env_id": "Taxi-v3", "mean_reward": 7.56, "n_eval_episodes": 100, "eval_datetime": "2022-08-08T09:21:52.747078"}
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