论文标题
通过图神经网络学习外围防御游戏的分散策略
Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks
论文作者
论文摘要
我们考虑为多代理外围防御游戏找到分散策略的问题。在这项工作中，我们设计了一个基于图形神经网络的学习框架，以从防御者的本地感知和通信图上学习映射到防御者的行动，以使学习的动作接近是由集中式专家算法产生的。我们证明，我们提议的网络更靠近专家政策，并且通过捕获更多的入侵者来优于其他基线算法。我们的基于GNN的网络经过小规模训练，可以概括为大型。为了验证我们的结果，我们在具有不同团队规模和初始配置的方案中运行外围防御游戏，以评估学习网络的性能。
We consider the problem of finding decentralized strategies for multi-agent perimeter defense games. In this work, we design a graph neural network-based learning framework to learn a mapping from defenders' local perceptions and the communication graph to defenders' actions such that the learned actions are close to that generated by a centralized expert algorithm. We demonstrate that our proposed networks stay closer to the expert policy and are superior to other baseline algorithms by capturing more intruders. Our GNN-based networks are trained at a small scale and can generalize to large scales. To validate our results, we run perimeter defense games in scenarios with different team sizes and initial configurations to evaluate the performance of the learned networks.
