Upload q-learning trained agent for FrozenLake-v1-4x4-slippery

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+tags:
+- FrozenLake-v1-4x4
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: q-learning-FrozenLake-v1-4x4-slippery-params-v3
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 0.90 +/- 0.30
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FrozenLake-v1-4x4
+      type: FrozenLake-v1-4x4
+---
+
+  # **Q-Learning** Agent playing **FrozenLake-v1-4x4-slippery**
+  This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1-4x4-slippery** .
+  
+  ## Usage
+  ```python
+  model = load_from_hub(repo_id="linker81/q-learning-FrozenLake-v1-4x4-slippery-params-v3", 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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+oid sha256:aa654981e7d10e2e7c147f078755bc39287b24acea3507c02eb3cdd06ca38e09
+size 914

replay.mp4

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+size 32779

results.json

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+{"env_id": "FrozenLake-v1", "mean_reward": 0.9036144578313253, "n_eval_episodes": 100, "eval_datetime": "2022-05-22T08:03:12.226059"}