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)

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)

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

@misc{alphaapple2024,
  title={AlphaApple: AI Agent for FruitBox Puzzle Game},
  author={Your Name},
  year={2024},
  howpublished={\url{https://huggingface.co/AlphaApple}}
}