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In most existing studies on large-scale multi-agent coordination, the controlmethods aim to learn discrete policies for agents with finite choices. Theyrarely consider selecting actions directly from continuous action spaces toprovide more accurate control, which makes them unsuitable for more complextasks. To solve the control issue due to large-scale multi-agent systems withcontinuous action spaces, we propose a novel MARL coordination control methodthat derives stable continuous policies. By optimizing policies with maximumentropy learning, agents improve their exploration in execution and acquire anexcellent performance after training. We also employ hierarchical graphattention networks (HGAT) and gated recurrent units (GRU) to improve thescalability and transferability of our method. The experiments show that ourmethod consistently outperforms all baselines in large-scale multi-agentcooperative reconnaissance tasks.

Entropy Enhanced Multi-Agent Coordination Based on Hierarchical Graph Learning for Continuous Action Space