Hierarchical Cognitive Learning for Cooperative Multi-agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (MARL) aims to enable agents to achieve coordinated behaviors in complex environments. However, existing methods often struggle to strike a balance between rapid local adaptation and consistent long-term cooperation, resulting in poor coordination. To address this challenge, we propose Hierarchical Cognitive Learning (HCL), a framework inspired by hierarchical mechanisms of human decision-making. This framework models hierarchical cognitive processes by integrating short-term perceptual alignment for responsive coordination, long-term intent modeling for strategic abstraction, and cognitive policy generation that unifies both pathways to produce coherent joint behaviors. This design allows agents to dynamically balance immediate reactions and long-horizon planning under partial observability. We evaluate HCL on the Active Voltage Control (AVC) task, the StarCraft Multi-Agent Challenge (SMAC) benchmark, and the SMAC-Hard benchmark, covering both continuous and discrete action spaces. Experimental results demonstrate that HCL outperforms state-of-the-art methods, facilitating effective multi-agent coordination.