多智能体对抗博弈中基于扩散-强化学习的分层运动规划
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games
AI总结：
针对部分可观测多智能体追逃博弈中的规避目标运动规划问题，提出融合高层扩散模型与低层强化学习策略的分层架构，显著提升探测率、目标到达率及策略可解释性、灵活性与效率。
摘要：
基于强化学习（RL）的运动规划近期已展现出在自主导航到机器人操作等任务中超越传统方法的潜力。在本工作中，我们聚焦于部分可观测多智能体对抗追逃博弈（PEG）中规避目标的运动规划任务。追逃问题与多种应用相关，例如搜索救援行动和监视机器人，在这些场景中，机器人必须有效规划自身行动以收集情报或完成使命任务，同时避免被探测或捕获。我们提出一种分层架构，该架构集成高层扩散模型，根据环境数据规划全局路径，而低层RL策略则对规避行为与全局路径跟随行为进行推理。在不同领域和不同可观测性条件下的基准测试结果表明，我们的方法在探测率和目标到达率上分别比基线高出77.18%和47.38%，从而使性能得分平均提升51.4%。此外，我们的方法提高了所学策略的可解释性、灵活性和效率。
英文摘要：
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 game (PEG). 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. 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 policy reasons about evasive versus global path-following behavior. The benchmark results across different domains and different observability show that our approach outperforms baselines by 77.18% and 47.38% on detection and goal reaching rate, which leads to 51.4% increasing of the performance score on average. Additionally, our method improves interpretability, flexibility and efficiency of the learned policy.
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