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

页码：6623 / 6626

页数：4

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