论文标题
使用多机构强化学习的行人环境中的多机器人社会意识合作计划
Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning
论文作者
论文摘要
在行人参与环境中，多个机器人的安全有效共同计划有望用于应用。在这项工作中，提出了一个新型的多机器人社会意识的有效合作计划者，该计划基于偏离多方面的多代理强化学习（MARL）在局部变化的观察和不完美的看法条件下。我们采用基于时间空间图（TSG）的社会编码器，以更好地提取每个机器人与行人在其视野（FOV）中的社会关系的重要性（FOV）。此外，我们在多机器人RL框架中介绍K-Step LookAhead奖励设置，以避免机器人产生的侵略性，侵入性，短视和非自然的运动决策。此外，我们通过多头全球注意模块改善了传统的集中式批评网络，以更好地汇总不同机器人之间的本地观察信息，以指导个人策略更新的过程。最后，多组实验结果验证了所提出的合作运动计划者的有效性。
Safe and efficient co-planning of multiple robots in pedestrian participation environments is promising for applications. In this work, a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV). Also, we introduce K-step lookahead reward setting in multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with multi-head global attention module to better aggregates local observation information among different robots to guide the process of individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner.
