| 基于强化学习的无人坦克对战仿真研究 |
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| 引用本文： | 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. |
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| 作者姓名： | 徐志雄 曹 雷 陈希亮 |
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| 作者单位： | 解放军理工大学 指挥信息系统学院，南京 210000 |
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| 摘 要： | 对标准的强化学习进行改进，通过引入动机层，来引入先验知识，加快学习速度。策略迭代选择上，通过采用“同策略”迭代的Sarsa学习算法，代替传统的“异策略”Q学习算法。提出了基于多动机引导的Sarsa学习（MMSarsa）算法，分别和Q学习算法、Sarsa学习算法在坦克对战仿真问题上进行了三种算法的对比实验。实验结果表明，基于多动机引导的Sarsa学习算法收敛速度快且学习效率高。
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| 关 键 词： | 多动机引导 Q学习 Sarsa学习 无人坦克 对战仿真 |
Research on unmanned tank battle simulation based on reinforcement learning |
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| XU Zhixiong,CAO Lei,CHEN Xiliang.Research on unmanned tank battle simulation based on reinforcement learning[J].Computer Engineering and Applications,2018,54(8):166-171. |
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| Authors: | XU Zhixiong CAO Lei CHEN Xiliang |
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| Affiliation: | Institute of Command Information System, PLA University of Science and Technology, Nanjing 210000, China |
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| Abstract: | To improve the classic reinforcement learning, through the introduction of motivation, prior knowledge is introduced, and the learning speed is speeded up. As to the iteration strategy, it adopts “on-policy” iterative Sarsa learning algorithm 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. |
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| Keywords: | multi-motivation guidance Q learning Sarsa learning unmanned tank battle simulation |
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