Research and Implementation of Hybrid Intelligent Wargame Based on Prior Knowledge-DQN Algorithm

Yuxiang Sun; Bo Yuan; Tao Zhang; Bojian Tang; Wanwen Zheng; Xianzhong Zhou

Working Paper Article Version 1 This version is not peer-reviewed

Version 1 : Received: 28 August 2020 / Approved: 31 August 2020 / Online: 31 August 2020 (04:08:04 CEST)

The reinforcement learning problem of complex action control in the Multi-player wargame is a hot research topic in recent years. In this paper , a game system based on turn-based confrontation is designed and implemented with the state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based the DQN(Deep Q Network) to model the complex game behaviors. Then, a priori- knowledge based algorithm PK-DQN(Prior Knowledge- Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate, the correctness of the PK-DQN algorithm is validated and its performance surpass the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deduction.

DQN Algorithm; Policy Modeling; Prior Knowledge; Intelligent Decision

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Research and Implementation of Hybrid Intelligent Wargame Based on Prior Knowledge-DQN Algorithm

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