Upload folder using huggingface_hub

README.md

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+---
+tags:
+- reinforcement-learning
+- q-learning
+- gymnasium
+- custom-implementation
+model-index:
+- name: q-FrozenLake-v1-4x4-noSlippery
+  results:
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      type: FrozenLake-v1-4x4-no_slippery
+      name: FrozenLake-v1-4x4-no_slippery
+    metrics:
+    - type: mean_reward
+      name: Mean reward
+      value: 1.000000
+    - type: success_rate
+      name: Success rate
+      value: 100.000000
+---
+
+# Q-Learning Agent playing FrozenLake-v1-4x4-no_slippery
+
+This repository contains a tabular Q-Learning agent trained on
+**FrozenLake-v1-4x4-no_slippery** using Gymnasium.
+
+The agent was implemented as part of the Hugging Face Deep
+Reinforcement Learning Course.
+
+## Evaluation results
+
+| Metric | Result |
+|---|---:|
+| Evaluation episodes | 1000 |
+| Mean reward | 1.0000 |
+| Reward standard deviation | 0.0000 |
+| Success rate | 100.00% |
+| Mean episode length | 6.00 |
+
+## Agent replay
+
+![Q-Learning agent replay](replay.gif)
+
+## Repository files
+
+- `q-learning.pkl`: Q-table, environment settings and hyperparameters
+- `qtable.npy`: NumPy Q-table
+- `results.json`: evaluation summary
+- `evaluation_episodes.csv`: individual evaluation episodes
+- `training_history.csv`: training history, when available
+- `replay.gif`: animated agent replay
+- `replay.mp4`: video replay, when available
+
+## Load the trained agent
+
+```python
+import pickle
+import gymnasium as gym
+
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download(
+    repo_id="a1914114315/q-FrozenLake-v1-4x4-noSlippery",
+    filename="q-learning.pkl"
+)
+
+with open(model_path, "rb") as file:
+    model = pickle.load(file)
+
+qtable = model["qtable"]
+
+env = gym.make(
+    model["env_id"],
+    **model.get("env_kwargs", {})
+)
+
+print("Environment:", model["env_id"])
+print("Q-table shape:", qtable.shape)
+print(
+    "Mean reward:",
+    model["evaluation"]["mean_reward"]
+)
+```
+
+## Training configuration
+
+```json
+{
+  "n_training_episodes": 10000,
+  "max_steps": 99,
+  "learning_rate": 0.7,
+  "gamma": 0.95,
+  "max_epsilon": 1.0,
+  "min_epsilon": 0.05,
+  "decay_rate": 0.0005,
+  "seed": 42
+}
+```

evaluation_episodes.csv

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qtable.npy

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results.json

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+{
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+  "eval_seed": 10000,
+  "eval_datetime": "2026-07-31T07:32:24.268371+00:00"
+}