论文标题
使用代理类信息专门针对异质多机构增强学习学习的代理间沟通
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information
论文作者
论文摘要
受代理通信与图神经网络的最新进展的启发，这项工作提出了将多代理通信能力作为定向标记的异质剂图的表示，其中节点标签表示代理类别和边缘标签，这是两个类别类别之间的通信类型。我们还介绍了一种神经网络体系结构，该架构专门通过学习单个转换到每对代理类之间的交换消息，从而在完全合作异质的多代理任务中进行沟通。通过使用具有异构代理的环境的参数共享的编码和操作选择模块，我们在大量代理类运行的环境中表现出可比性或卓越的性能。
Inspired by recent advances in agent communication with graph neural networks, this work proposes the representation of multi-agent communication capabilities as a directed labeled heterogeneous agent graph, in which node labels denote agent classes and edge labels, the communication type between two classes of agents. We also introduce a neural network architecture that specializes communication in fully cooperative heterogeneous multi-agent tasks by learning individual transformations to the exchanged messages between each pair of agent classes. By also employing encoding and action selection modules with parameter sharing for environments with heterogeneous agents, we demonstrate comparable or superior performance in environments where a larger number of agent classes operates.
