Multi-agent systems often face challenges such as elevated communication demands, intricate interactions, and difficulties in transferability. To address the issues of complex information interaction and model scalability, we propose an innovative hierarchical graph attention actor–critic reinforcement learning method. This method naturally models the interactions within a multi-agent system as a graph, employing hierarchical graph attention to capture the complex cooperative and competitive relationships among agents, thereby enhancing their adaptability to dynamic environments. Specifically, graph neural networks encode agent observations as single feature-embedding vectors, maintaining a constant dimensionality irrespective of the number of agents, which improves model scalability. Through the “inter-agent” and “inter-group” attention layers, the embedding vector of each agent is updated into an information-condensed and contextualized state representation, which extracts state-dependent relationships between agents and model interactions at both individual and group levels. We conducted experiments across several multi-agent tasks to assess our proposed method’s effectiveness, stability, and scalability. Furthermore, to enhance the applicability of our method in large-scale tasks, we tested and validated its performance within a curriculum learning training framework, thereby enhancing its transferability.

Multi-agent reinforcement learning has been widely applied in solving various sequential decision-making problems in recent years. However, a key challenge is sparse rewards, where agents receive meaningful reward signals only upon task completion. This issue leads to inefficient exploration and slow learning progress. To address this problem, we propose an excellent-student learning method supported by a decentralized distributed learning paradigm, drawing inspiration from academically diverse classrooms. Specifically, we design a novel excellent-student learning model, which suggests that agents mimic the learning behaviors of excellent students. This model requires agents to share knowledge with other agents and engage in individual exploration driven by curiosity. Next, to foster collaborative team learning behaviors, similarity measurement techniques are integrated to enhance knowledge sharing among agents. An intrinsic reward function is designed, combining individual exploration with whole-class sharing, providing additional motivation for discovering new actions and states. This reward function is seamlessly incorporated into the policy learning process. Finally, experiments conducted in various multi-agent particle environments demonstrate significant improvements in training efficiency and stability.