论文标题
基于连续动作空间的层次图学习，熵增强了多代理协调
Entropy Enhanced Multi-Agent Coordination Based on Hierarchical Graph Learning for Continuous Action Space
论文作者
论文摘要
在大多数有关大规模多代理协调的研究中，控制方法旨在学习有限选择的代理商的离散政策。他们很少考虑直接从连续的动作空间中选择动作以提供更准确的控制，这使得它们不适合更复杂的任务。为了解决具有连续作用空间的大规模多机构系统引起的控制问题，我们提出了一种新型的MARL配位控制方法，该方法得出了稳定的连续策略。通过通过最大的熵学习来优化政策，代理商改善了他们在执行方面的探索，并在训练后获得出色的表现。我们还采用分层图注意网络（HGAT）和门控复发单元（GRU）来提高方法的可扩展性和可传递性。实验表明，我们的方法在大型多机构合作侦察任务中始终优于所有基准。
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.
