Knowledge Commons of Institute of Automation，CAS
| ADP with MCTS algorithm for Gomoku | |
Tang Zhentao ; Zhao Dongbin; Shao Kun; Lv Le
| |
| 2017-02 | |
| 会议名称 | The 2016 IEEE Symposium Series on Computational Intelligence |
| 会议日期 | 6-9 Dec. 2016 |
| 会议地点 | Athens, Greece |
| 摘要 | Inspired by the core idea of AlphaGo, we combine a neural network, which is trained by Adaptive Dynamic Programming (ADP), with Monte Carlo Tree Search (MCTS) algorithm for Gomoku. MCTS algorithm is based on Monte Carlo simulation method, which goes through lots of simulations and generates a game search tree. We rollout it and search the outcomes of the leaf nodes in the tree. As a result, we obtain the MCTS winning rate. The ADP and MCTS methods are used to estimate the winning rates respectively. We weight the two winning rates to select the action position with the maximum one. Experiment result shows that this method can effectively eliminate the neural network evaluation function's “short-sighted” defect. With our proposed method, the game's final prediction result is more accurate, and it outperforms the Gomoku with ADP algorithm. |
| DOI | 10.1109/SSCI.2016.7849371 |
| 引用统计 | |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/14475 |
| 专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
| 作者单位 | The State Key Laboratory of Management and Control for Complex Systems. Institute of Automation, Chinese Academy of Sciences. Beijing 100190, China |
| 推荐引用方式 GB/T 7714 | Tang Zhentao,Zhao Dongbin,Shao Kun,et al. ADP with MCTS algorithm for Gomoku[C],2017. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| 07849371.pdf（866KB） | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论