Upload README.md with huggingface_hub

README.md

@@ -1,3 +1,110 @@
----
-license: mit
----
+---
+library_name: stable-baselines3
+tags:
+- FruitBox
+- reinforcement-learning
+- ppo
+- game-ai
+- puzzle-solving
+model-index:
+- name: AlphaApple
+  results:
+  - task:
+      type: reinforcement-learning
+      name: Reinforcement Learning
+    dataset:
+      name: FruitBox Game
+      type: fruitbox
+    metrics:
+    - type: mean_reward
+      value: 77.0
+      name: Mean Episode Score
+    - type: improvement_vs_random
+      value: 7.1%
+      name: Improvement vs Random
+    - type: improvement_vs_greedy  
+      value: 5.0%
+      name: Improvement vs Greedy
+---
+
+# AlphaApple: FruitBox Game AI Agent
+
+## Model Description
+
+이 모델은 한국의 사과게임(FruitBox) 퍼즐을 해결하는 AI 에이전트입니다. 
+10×17 격자에서 합이 10인 직사각형을 찾아 제거하는 게임을 PPO(Proximal Policy Optimization) 알고리즘으로 학습했습니다.
+
+## Game Rules
+
+- 10×17 격자, 각 셀은 1-9 숫자
+- 직사각형 영역을 선택해서 숫자 합이 정확히 10이면 해당 영역 제거
+- 제거된 셀 개수만큼 점수 획득
+- 더 이상 제거할 수 있는 영역이 없으면 게임 종료
+
+## Performance
+
+| Agent   | Average Score | Improvement |
+|---------|--------------|-------------|
+| Random  | 71.9         | -           |
+| Greedy  | 73.3         | +1.9%       |
+| **PPO** | **77.0**     | **+7.1%**   |
+
+## Usage
+
+### Python (PyTorch)
+
+```python
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv
+
+# Load model
+model = PPO.load("pytorch_model.zip")
+
+# Use for inference
+obs = env.reset()
+action, _ = model.predict(obs)
+```
+
+### Web/JavaScript (ONNX)
+
+```javascript
+import { InferenceSession } from 'onnxruntime-web';
+
+// Load ONNX model
+const session = await InferenceSession.create('./fruitbox_ppo.onnx');
+
+// Predict action
+const { action_logits } = await session.run({
+    board_input: new ort.Tensor('float32', board_data, [1, 17, 10, 1])
+});
+const action = action_logits.data.indexOf(Math.max(...action_logits.data));
+```
+
+## Files
+
+- `pytorch_model.zip`: Original SB3 PPO model 
+- `fruitbox_ppo.onnx`: ONNX version for web deployment (2.95MB)
+- `model_info.json`: Model metadata and performance metrics
+
+## Training Details
+
+- Algorithm: PPO with action masking
+- Network: Custom CNN (SmallGridCNN)
+- Training steps: 1,000,000
+- Environment: Custom Gymnasium environment
+- Action space: 8,415 possible rectangles (masked)
+
+## Repository
+
+Source code: https://github.com/your-username/alphaapple
+
+## Citation
+
+```bibtex
+@misc{alphaapple2024,
+  title={AlphaApple: AI Agent for FruitBox Puzzle Game},
+  author={Your Name},
+  year={2024},
+  howpublished={\url{https://huggingface.co/AlphaApple}}
+}
+```