真实的自我游戏
Truthful Self-Play
摘要：
我们提出了一个进化学习的一般框架，以紧急无偏状态表示没有任何监督。进化框架，如自我发挥收敛到坏的局部最优的情况下，多智能体强化学习的非合作部分可观察的环境中，由于信息不对称的通信。我们提出的框架是一个简单的修改自我发挥机制设计的启发，也被称为{ \em反向博弈理论}，引出真实的信号，使代理合作。其关键思想是使用对等预测方法添加虚拟奖励，即，一种用于评估分散环境中代理之间交换的信息的有效性的机制。通过捕食者-被捕食者、交通路口和星际争霸任务的数值实验，证明了该框架的最佳性能。
英文摘要：
We present a general framework for evolutionary learning to emergent unbiased state representation without any supervision. Evolutionary frameworks such as self-play converge to bad local optima in case of multi-agent reinforcement learning in non-cooperative partially observable environments with communication due to information asymmetry. Our proposed framework is a simple modification of self-play inspired by mechanism design, also known as {\em reverse game theory}, to elicit truthful signals and make the agents cooperative. The key idea is to add imaginary rewards using the peer prediction method, i.e., a mechanism for evaluating the validity of information exchanged between agents in a decentralized environment. Numerical experiments with predator prey, traffic junction and StarCraft tasks demonstrate that the state-of-the-art performance of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
