elfray commited on
Commit
aa1c565
1 Parent(s): 713c0cf

Push Q-Learning agent to Hub

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Files changed (5) hide show
  1. .gitattributes +2 -0
  2. README.md +35 -0
  3. q-learning.pkl +3 -0
  4. replay.mp4 +3 -0
  5. results.json +1 -0
.gitattributes CHANGED
@@ -25,3 +25,5 @@ saved_model/**/* 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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  *.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
README.md ADDED
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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: 7.46 +/- 2.76
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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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+
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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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+
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+ ## Usage
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+ ```python
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+ model = load_from_hub(repo_id="elfray/q-Taxi-v3", filename="q-learning.pkl")
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+
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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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+ evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
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+
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+ ```
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+
q-learning.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:06afd8b947601248c5a5309c0f3be9e6726ff9dccc7b1e8fad6bac9b296a9a7f
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+ size 24589
replay.mp4 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:bd22842e824a8b1c1cb9726b608cd12c16fc6df0ad1470bd7596527139d79d17
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+ size 103093
results.json ADDED
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+ {"env_id": "Taxi-v3", "mean_reward": 7.46, "n_eval_episodes": 100, "eval_datetime": "2022-06-02T10:58:25.195416"}