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.

基于强化学习的运动规划在自主导航到机器人操控等方面已显示出超越传统方法的潜力。本文针对部分可观察多智能体对抗潜逃游戏（PEG）中规划机动任务展开研究。我们提出了一种分层架构，将高层扩散模型与低层强化学习算法结合，分别用于全局路径规划和回避行为推理。该方法通过利用扩散模型引导强化学习算法进行更高效的探索，并提高了可解释性和预测能力，相较于基准模型的表现提高了 51.2%。