This paper addresses the challenging problem of multi-agent dynamic target pursuit under stringent communication constraints (including delays and range limits), where the agile targets are non-cooperative and free from such limitations. To tackle this, we propose CRS-DQN, a novel Deep Q-Network algorithm designed for this scenario. CRS-DQN enables agents to learn effective pursuit strategies through deep reinforcement learning despite partial observability and constrained information sharing. Simulation experiments systematically evaluate the impact of key parameters. The results show that pursuit performance degrades monotonically with increased communication delay. In contrast, the communication radius exhibits a non-linear effect: performance peaks when the radius is within a specific range (approximately 1/10 to 1/5 of the environment size) and declines if the radius is too small or too large. Furthermore, an optimal balance exists between the communication radius and the delay threshold. This work demonstrates the feasibility of learning-based pursuit under strict communication constraints and provides insights into parameter tuning for robust multi-agent systems in adversarial, communication-degraded environments.
Yu et al. (Thu,) studied this question.