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.

翻译：我们提出了一种通用的进化学习框架，可在无任何监督的情况下涌现无偏的状态表征。在非合作、部分可观测的通信环境中，由于信息不对称，自博弈等进化框架在多智能体强化学习中会收敛至不良局部最优解。受机制设计（亦称逆向博弈论）启发，我们提出的框架是对自博弈的简单改进，旨在激发真实信号并使智能体协同合作。核心思想是利用同行预测方法添加虚构奖励——即一种在去中心化环境中评估智能体间信息交换有效性的机制。在捕食者-猎物、交通枢纽及星际争霸任务上的数值实验表明，我们的框架达到了最先进的性能水平。