Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment
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AbstractThe reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an 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, it is validated, and its performance surpasses 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
CitationSun, Y., Yuan, B., Zhang, T., Tang, B., Zheng, W. and Zhou, X., (2020). 'Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment'. Electronics, 9(10), pp. 1-21.
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