Deep Reinforcement Learning Policy in Hex Game System

被引：0
|
作者：
Lu, Mengxuan [1 ]
Li, Xuejun [1 ]
机构：
[1] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China
来源：
PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年
关键词：
Computer Game; Hex Game; Deep Reinforcement Learning; Actor-Critic A3C; GO;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Hex game is a zero-sum chess game. It has a large solution space when using 11 x 11 size of chess board. In recent years, deep reinforcement learning -based Go game systems, i.e. AlphaGo and AlphaGo Zero, have gotten huge achievement. In this paper, we design the self-learning method and system structure of Hex game. design policy network and value network referred to residual network, and use asynchronous advantage actor-critic algorithm to train policy network and value network. The comparison of deep reinforcement learning-based policy network and fixed strategy proves better effect of self-learning.
引用
收藏
页码：6623 / 6626
页数：4
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