Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games

arXiv:2403.10794v1 Announce Type: cross
Abstract: Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation. In this work, we focus on a motion planning task for an evasive target in a partially observable multi-agent adversarial pursuit-evasion games (PEG). These pursuit-evasion problems are relevant to various applications, such as search and rescue operations and surveillance robots, where robots must effectively plan their actions to gather intelligence or accomplish mission tasks while …

abstract adversarial agent arxiv autonomous cs.lg cs.ma cs.ro diffusion evasion focus games hierarchical manipulation motion planning multi-agent navigation observable planning reinforcement reinforcement learning robot robot manipulation type work