Truthful Self-Play

Shohei Ohsawa

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arXiv · 2106.03007

Truthful Self-Play

Shohei Ohsawa

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Abstract

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.

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stat.ML cs.LG cs.NE econ.EM

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BibTeX RIS

Shohei Ohsawa. 2021-06-06. Truthful Self-Play. https://arxiv.org/abs/2106.03007

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