Swarm Confrontation

Swarm confrontation research focuses on developing algorithms and control strategies for groups of autonomous agents engaged in competitive scenarios, such as pursuit-evasion games. Current efforts concentrate on improving the robustness and scalability of these strategies using hierarchical reinforcement learning, multi-agent reinforcement learning with graph convolutional networks, and physics-informed neural networks to handle high uncertainty and large-scale interactions. This research is significant for advancing autonomous systems in diverse fields, including defense, disaster response, and robotics, by enabling more effective coordination and control of swarms in complex, dynamic environments.