论文标题
磁铁：深度多代理增强学习的多代理图网络
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
论文作者
论文摘要
近年来，深入的强化学习在复杂的单一代理任务中表现出了很大的成功，而最近，这种方法也应用于多机构领域。在本文中，我们提出了一种称为磁铁的新方法，以使用自我注意机制获得的环境的相关图表和消息生成技术，以使用相关的图表表示。我们将磁铁方法应用于合成捕食者 - 捕集的多区域环境和pommerman游戏，结果表明，它大大优于最先进的MARL解决方案，包括多代理Q-Networks（MADQN），多代理的深层确定性政策梯度（MADDPG）和QMIX（MADDPG）和
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGNet, to multi-agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique. We applied our MAGnet approach to the synthetic predator-prey multi-agent environment and the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including Multi-agent Deep Q-Networks (MADQN), Multi-agent Deep Deterministic Policy Gradient (MADDPG), and QMIX
