计算机工程与应用2018，Vol.54Issue(8)：166-171,6.DOI:10.3778/j.issn.1002-8331.1610-0348
基于强化学习的无人坦克对战仿真研究
Research on unmanned tank battle simulation based on reinforcement learning
摘要
Abstract
To improve the classic reinforcement learning,through the introduction of motivation,prior knowledge is intro-duced,and the learning speed is speeded up.As to the iteration strategy,it adopts"on-policy"iterative Sarsa learning algo-rithm instead of traditional"off-policy"Q learning algorithm. It proposes Multi-Motivation Sarsa learning algorithm (MMSarsa)and respectively carries out the comparative tests on tank battle simulation with Q-learning algorithm and Sarsa learning algorithm.The results of experiment show that Sarsa learning algorithm based on motivation guidance has fast convergence rate and high learning efficiency.关键词
多动机引导/Q学习/Sarsa学习/无人坦克/对战仿真Key words
multi-motivation guidance/Q learning/Sarsa learning/unmanned tank/battle simulation分类
信息技术与安全科学引用本文复制引用
徐志雄,曹雷,陈希亮..基于强化学习的无人坦克对战仿真研究[J].计算机工程与应用,2018,54(8):166-171,6.基金项目
国家部委预研基金. （）
