AlphaZero-based Proof Cost Network to Aid Game Solving
Authors: Ti-Rong Wu, Chung-Chin Shih, Ting Han Wei, Meng-Yu Tsai, Wei-Yuan Hsu, I-Chen Wu
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on solving 15x15 Gomoku and 9x9 Killall-Go problems with both MCTS-based and focused depth-first proof number search solvers. Comparisons between using Alpha Zero networks and PCN as heuristics show that PCN can solve more problems. |
| Researcher Affiliation | Academia | 1Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan 2Research Center for Information Technology Innovation, Academia Sinica, Taiwan 3Department of Computing Science, University of Alberta, Edmonton, Canada |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All experiments can be reproduced by following the instructions in the README file on https://github.com/kds285/proof-cost-network, including source code, problem sets, MCTS and FDFPN solvers, and the trained models used in this paper. |
| Open Datasets | Yes | All experiments can be reproduced by following the instructions in the README file on https://github.com/kds285/proof-cost-network, including source code, problem sets, MCTS and FDFPN solvers, and the trained models used in this paper. For 15x15 Gomoku, we choose Yixin (Sun, 2018)...For 9x9 Killall-Go, since there are no open-source 9x9 Killall-Go programs, we simply use α0 and PCN-bmax to generate self-play games. |
| Dataset Splits | No | The paper mentions collecting data via self-play for training and using generated problem sets for evaluation. However, it does not specify explicit train/validation/test splits of these datasets for network training, nor does it specify any cross-validation setup. |
| Hardware Specification | Yes | We use 1080Ti GPUs for training, where the network is implemented with Py Torch (Paszke et al., 2019)... Each solver runs with one CPU and one NVIDIA Tesla V100. |
| Software Dependencies | No | The paper mentions 'implemented with Py Torch (Paszke et al., 2019)' but does not provide specific version numbers for PyTorch or any other software libraries or solvers. |
| Experiment Setup | Yes | We run 400 MCTS simulations for each move during self-play, for a total of 1,500,000 games, and the network is optimized every 5,000 games. The network contains 5 residual blocks with 64 filters, is optimized by SGD with 0.9 for momentum, 1e-4 for weight decay, and a fixed learning rate of 0.02. |