AlphaApple - FruitBox DQN

This model plays the FruitBox (Fruit Box) puzzle game hosted on Gamesaien. It predicts Q-values over all axis-aligned rectangles on a 10x17 board. A valid action is a rectangle whose cell sum is exactly 10, so you must apply an action mask to filter invalid rectangles before selecting the best move.

Quick facts

Board: 10x17, values 0-9 (0 means empty)
Action space: 8415 axis-aligned rectangles
Input: one-hot board with shape [1, 10, 10, 17]
Output: Q-values for all rectangles
Masking: required to remove invalid rectangles

Files in this repo

model.pth: PyTorch checkpoint dict with policy_net, target_net, optimizer
model.onnx: Exported ONNX model for browser/runtime inference

How to use (PyTorch)

# Model definition is in https://github.com/kbsooo/AlphaApple (src/models.py)
import torch
from src.models import FruitBoxDQN

rows, cols = 10, 17
n_actions = 55 * 153  # (rows*(rows+1)/2) * (cols*(cols+1)/2) = 8415
model = FruitBoxDQN(rows, cols, n_actions)

ckpt = torch.load("model.pth", map_location="cpu")
state = ckpt["policy_net"] if "policy_net" in ckpt else ckpt
model.load_state_dict(state)
model.eval()

How to use (ONNX / browser)

const session = await ort.InferenceSession.create("model.onnx");
// input: Float32Array with shape [1, 10, 10, 17]
const output = await session.run({ input });
// output.output.data: Q-values for 8415 rectangles

Action masking (required)

You must mask invalid rectangles before selecting an action. A rectangle is valid if the sum of its cells equals 10. Without the mask, the model can pick illegal moves.

Training details

Environment: FruitBoxEnv (implemented in envs/fruitbox_env.py, class FruitBoxEnvImproved)
Board generator: BackwardBoardGenerator (solvable boards)
Curriculum: target coverage ramps from 0.3 to 0.95 in steps of 0.1
Optimizer: Adam, gamma=0.99, lr=1e-4
Episodes: 10k (Colab integrated script)

Limitations

Trained on generated boards; performance may vary on edge cases.
Requires an accurate action mask and correct board extraction.

kbsooo
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AlphaApple