基于MATD3算法的多智能体避碰控制
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引用本文:郭雷1,梁成庆2.基于MATD3算法的多智能体避碰控制[J].计算技术与自动化,2024,(1):9-15
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作者单位
郭雷1,梁成庆2 (1.河海大学 理学院, 江苏 南京 211100
2.河海大学 人工智能与自动化学院, 江苏 南京 211100) 
中文摘要:使用多智能体双延迟深度确定性策略梯度(Multi-agent Twin Delayed Deep Deterministic Policy Gradient,MATD3)算法研究了多无人机的避障和到达目标点问题,首先,利用MATD3算法的优越性提高训练效率。其次,基于人工势场法的思想设计了稠密碰撞奖励函数,使得智能体在没有找到最优解决方案时也能得到积极的反馈,加快学习速度。最后,在仿真实验阶段,通过设计的三组对比实验和泛化实验验证了算法的有效性。
中文关键词:多智能体  强化学习  人工势场法  避障
 
Multi-agent Collision Avoidance Control Based on MATD3 Algorithm
Abstract:Multi-agent twin delayed deep deterministic policy gradient(MATD3) algorithm is used to study the obstacle avoidance and target reaching of UAVs. Firstly, the advantage of MATD3 algorithm is used to improve the training efficiency. Secondly, a dense reward function is designed based on the idea of artificial potential field, which can accelerate the learning speed and help the agent get positive feedback when the optimal solution is not found. In the experiment, the effectiveness of the algorithm is verified by the comparison experiment and generalization experiment.
keywords:multi-agent  reinforcement learning  artificial potential field  obstacle avoidance
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