论文标题
在对手的存在下学习复杂的多代理政策
Learning Complex Multi-Agent Policies in Presence of an Adversary
论文作者
论文摘要
近年来，将深入的强化学习应用于多代理设置方面有一些杰出的工作。通常，在这种多代理场景中，对手可能会出现。我们通过实施基于图的多代理深钢筋学习算法来解决这种设置的要求。在这项工作中，我们考虑了多代理欺骗的情况，在这些情况下，多个代理人需要学会合作和交流以欺骗对手。我们已经采用了两个阶段的学习过程来吸引合作的代理人学习这种欺骗性行为。我们的实验表明，我们的方法使我们能够采用课程学习来增加环境中的合作代理的数量，并使一组代理团队能够学习复杂的行为，以成功地欺骗对手。 关键字：多代理系统，图形神经网络，增强学习
In recent years, there has been some outstanding work on applying deep reinforcement learning to multi-agent settings. Often in such multi-agent scenarios, adversaries can be present. We address the requirements of such a setting by implementing a graph-based multi-agent deep reinforcement learning algorithm. In this work, we consider the scenario of multi-agent deception in which multiple agents need to learn to cooperate and communicate in order to deceive an adversary. We have employed a two-stage learning process to get the cooperating agents to learn such deceptive behaviors. Our experiments show that our approach allows us to employ curriculum learning to increase the number of cooperating agents in the environment and enables a team of agents to learn complex behaviors to successfully deceive an adversary. Keywords: Multi-agent system, Graph neural network, Reinforcement learning
