ARAC: Adaptive Regularized Multi-Agent Soft Actor-Critic in Graph-Structured Adversarial Games

The introduction of ARAC, or Adaptive Regularized Multi-Agent Soft Actor-Critic, marks a significant advancement in multi-agent reinforcement learning (MARL) for graph-structured adversarial tasks. This method addresses the challenge of sparse rewards that often hinder effective policy learning in dynamic environments. By employing an attention-based graph neural network (GNN), ARAC effectively models agent dependencies, allowing for a more expressive representation of spatial relations and state features. Furthermore, the adaptive divergence regularization mechanism enhances exploration during early training stages while minimizing reliance on potentially flawed reference policies as training progresses. Experimental results in pursuit and confrontation scenarios demonstrate that ARAC not only achieves faster convergence but also exhibits higher final success rates and improved scalability compared to traditional MARL baselines. This innovative approach is poised to enhance the effici…