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Parent(s):
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Push Q-Learning agent to Hub
Browse files- .gitattributes +2 -0
- README.md +35 -0
- q-learning.pkl +3 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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q-learning.pkl filter=lfs diff=lfs merge=lfs -text
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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-init
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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="RustBucket/q-Taxi-v3-init", 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 sha256:2b5e108e8542945315ce63a9e361638f1fd8e4d67a0b2065eee2740b9a247cec
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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 sha256:2aa1b830b627a46e01db90cece5fcec103559aeb1b6d58c50d29eba918b9a4b7
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size 105245
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results.json
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{"env_id": "Taxi-v3", "mean_reward": 7.56, "n_eval_episodes": 100, "eval_datetime": "2022-05-24T17:54:48.000386"}
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