Constrained multi-agent evasion using deep reinforcement learning

Abstract

Designing effective evasion strategies in pursuit–evasion scenarios is challenging, particularly when the pursuer’s model is unknown and inaccessible. This limitation hinders the application of conventional evasion policy design methods. To overcome this challenge, especially when evaders have constrained maneuverability against unrestricted pursuers, we propose a novel multi-agent evasion algorithm based on deep reinforcement learning. Our approach employs a staged learning framework, progressively guiding evaders from simpler to more complex tasks to refine their evasion strategies. Crucially, our algorithm enables evaders to infer pursuers’ intentions even without prior knowledge of pursuers’ objectives, allowing for optimal decision-making despite mobility constraints. Simulation results demonstrate that our method significantly enhances evasion success, validating the effectiveness of learning-based strategies. Additionally, the algorithm exhibits strong adaptability to environmental changes, ensuring reliable performance across diverse pursuit–evasion scenarios.