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papersSEP 10 04:00 UTC

Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning

A new arXiv paper examines permutation surrogate tests, a common tool for estimating directed influence between reinforcement learning agents, by validating them against known ground truth. In two multi-agent settings, a social dilemma and a coordination race, the authors find that non-stationarity in agent behavior undermines these surrogate methods. The study provides diagnostics and corrective approaches to make information-theoretic influence measures more reliable.

arXivdirected-influenceinformation-theoretic-influence-measuresmulti-agent-reinforcement-learningnon-stationaritypermutation-surrogates

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AINon-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies ↗SEP 10 04:00 UTC
arXiv cs.LGNon-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies ↗SEP 10 04:00 UTC

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