Deep reinforcement learning policy in Hex game system
Mengxuan Lu, Xuejun Li · 2018
Hex game is a zero-sum chess game. It has a large solution space when using 11×11 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.