Push Q-Learning agent to Hub

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.mp4 filter=lfs diff=lfs merge=lfs -text
+q-learning.pkl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- FrozenLake-v1-4x4-no_slippery
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: q-FrozenLake-v1-4x4-noSlippery
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1.00 +/- 0.00
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FrozenLake-v1-4x4-no_slippery
+      type: FrozenLake-v1-4x4-no_slippery
+---
+
+  # **Q-Learning** Agent playing **FrozenLake-v1**
+  This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
+  
+  ## Usage
+  ```python
+  model = load_from_hub(repo_id="comodoro/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
+
+  # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
+  env = gym.make(model["env_id"])
+
+  evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
+  
+  ```
+  

q-learning.pkl

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+size 933

replay.mp4

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+size 28656

results.json

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+{"env_id": "FrozenLake-v1", "mean_reward": 1.0, "n_eval_episodes": 100, "eval_datetime": "2022-05-23T08:23:26.655723"}