Title: Generative Integration Networks. Abstract: This paper presents an unbiased exploration framework for the belief state $p(s)$ in non-cooperative, multi-agent, partially-observable environments through differentiable recurrent functions. As well as single-agent exploration via intrinsic reward and generative RNNs, several researchers have proposed differentiable multi-agent communication models such as CommNet and IC3Net for scalable exploration through multiple agents. However, none of the existing frameworks so far capture the unbiased belief state in non-cooperative settings as with the nature due to  biased examples reported from adersarial agents.
{\em Generative integration networks} (GINs) is the first unbiased exploration framework insipired by honest reporting mechanisms in economics. The key idea is {\em synchrony}, an inter-agent reward to discriminate the honest reporting and the adversarial reporting \textbf{without real examples}, which is the different point from the GANs. Experimental results obtained using two non-cooperative multi-agent environments up to 20 agents denote that GINs show state-of-the-art performance in the exploration frameworks.
