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

Understand

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.