Kavli Affiliate: Ke Wang
| First 5 Authors: Yining Chen, Ke Wang, Guanghua Song, Xiaohong Jiang,
| Summary:
In most existing studies on large-scale multi-agent coordination, the control
methods aim to learn discrete policies for agents with finite choices. They
rarely consider selecting actions directly from continuous action spaces to
provide more accurate control, which makes them unsuitable for more complex
tasks. To solve the control issue due to large-scale multi-agent systems with
continuous action spaces, we propose a novel MARL coordination control method
that derives stable continuous policies. By optimizing policies with maximum
entropy learning, agents improve their exploration in execution and acquire an
excellent performance after training. We also employ hierarchical graph
attention networks (HGAT) and gated recurrent units (GRU) to improve the
scalability and transferability of our method. The experiments show that our
method consistently outperforms all baselines in large-scale multi-agent
cooperative reconnaissance tasks.
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