Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning

This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.