Generative Integration NetworksDownload PDF

25 Sept 2019 (modified: 05 May 2023)ICLR 2020 Conference Desk Rejected SubmissionReaders: Everyone
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Keywords: Synchrony, State Representations, Generative RNNs, Adversarial Learning, Multi-agent Communication
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.
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