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 avoiding detection or capture themselves. We propose a hierarchical architecture that integrates a high-level diffusion model to plan global paths responsive to environment data while a low-level RL algorithm reasons about evasive versus global path-following behavior. Our approach outperforms baselines by 51.2% by leveraging the diffusion model to guide the RL algorithm for more efficient exploration and improves the explanability and predictability.

翻译：强化学习（RL）驱动的运动规划近期在从自主导航到机器人操作等领域展现出超越传统方法的潜力。本研究聚焦于部分可观测多智能体对抗性追逃博弈（PEG）中逃逸目标的运动规划任务。这类追逃问题与搜索救援行动、监控机器人等多种应用场景密切相关，其中机器人必须有效规划行动以收集情报或完成任务，同时避免被侦查或捕获。我们提出一种分层架构，将高层扩散模型用于根据环境数据规划全局路径，而低层强化学习算法则负责推理逃逸行为与全局路径跟踪行为的抉择。通过利用扩散模型引导强化学习算法实现更高效的探索，该方法相较于基线方案性能提升51.2%，同时增强了可解释性与可预测性。