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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README.md

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+---
+tags:
+- Taxi-v3
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: q-Taxi-v3
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 7.52 +/- 2.73
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Taxi-v3
+      type: Taxi-v3
+---
+
+  # **Q-Learning** Agent playing **Taxi-v3**
+  This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
+  
+  ## Usage
+  ```python
+  model = load_from_hub(repo_id="AdoubleLen/q-Taxi-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:c1cb9d5500424188da02a2e2140ad9f1363eef940263d223b8386a116bcbd554
+size 24589

replay.mp4

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+oid sha256:6410e70a0d8ac0e4ad969b3be02a41681db61fac857bc215d5f77d8c2c517d61
+size 92566

results.json

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+{"env_id": "Taxi-v3", "mean_reward": 7.52, "n_eval_episodes": 100, "eval_datetime": "2022-07-21T20:40:56.058749"}