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Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies

arxiv.org/abs/2604.23716

Updated 51 min ago · first seen 11 Sept 2026

paper_01M294GNWH5V6TNVDY2XHBTGMX

Published
11 Sept 2026
T1 · 51 min ago
arXiv
2604.23716
T1 · 51 min ago
Category
cs.AI
T1 · 51 min ago

Abstract

Reporting guidance for information-theoretic measures is rarely tested against ground truth. We test one guardrail in two multi-agent reinforcement learning games, a social dilemma and a coordination race, where directed influence between selected agent pairs is zero by construction, over 100 seeds. Omitting one precondition, exclusion of the non-stationary training transient, gives false-positive rates of 100.00% and 99.95%: agents annealing exploration independently, in runs that never met, are flagged as influencing one another. Excluding the transient reaches 3.0% in the social dilemma but 11.8% in the coordination game, which stationarity tests explain: 95.7% of social-dilemma series are stationary afterwards against 56.8% of coordination series. So the non-stationarity must be treated, and exclusion is neither the only way nor sufficient. What we recommend instead changes the null model rather than the data: permuting the source within blocks of training time reaches 5.25% and 5.50%, the only one of four constructions at the size of the test in both games, leaving series, statistic and estimand untouched. Titrating injected links of known strength in both games shows it is also the most sensitive of the three, detecting 89.0% in the coordination game where conditioning detects 61.0% on identical pairs, while the ablated test reports 100% with or without a link, so its apparent sensitivity is uninformative. The block count is not critical: every setting from 16 to 256 lands in the nominal region, and a partition derived from the stationarity test removes the parameter, though less sensitively. Code and data are released.

Authors 2

Nikolaos Al. Papadopoulos, Konstantinos E. Psannis

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

Arxiv announce type
replace

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

arXiv id
2604.23716

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

Categories
cs.AI, cs.IT, cs.LG, cs.MA, math.IT

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

Primary category
cs.AI

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 51 min agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

51 min ago

Conflicts

None