  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.
