追求者
强化学习
追逃
计算机科学
运动学
移动机器人
人工智能
逃避(道德)
机器人
数学优化
数学
免疫系统
经典力学
生物
物理
免疫学
作者
Qi Qi,Xuebo Zhang,Xian Guo
标识
DOI:10.1109/rcar49640.2020.9303044
摘要
In a pursuit-evasion game, the pursuer tries to capture the evader, while the evader actively avoids being captured. Traditional approaches usually ignore or simplify kinematic constraints by using a grip world discrete model and they assume that the game is played in free space without obstacles. In this paper, a curriculum deep reinforcement learning approach is proposed for the pursuit-evasion game, which considers the kinematics of mobile robot in practical applications and the influence of static obstacles in the environment. To improve the system performance, we use the mechanism of self-play to train the pursuer and the evader at the same time. In addition, the method of curriculum learning is used, making the agent learn simpler tasks before learning more complicated ones. Comparative simulation results show that the proposed approach presents superior performance for both pursuer and evader when playing against intelligent opponents.
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