SOURCE-LINKED INTELLIGENCE

Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Cooperative multi-agent reinforcement learning (MARL) systems rely on past experience for learning coordinated behaviour, but this experience may become unreliable if the environment or task objective changes during training. In such cases, agents first need a way to recognize that the situation has changed before deciding how to adapt. This paper studies online change-point detection for cooperative MARL using reward-derived signals. We propose \emph{Patterns of Past Rewards} (PPR), a lightweight algorithm-agnostic detector that smooths agents' return streams, highlights recent changes, and a

Read original source ↗ Open in workspace

recordType

paper

region

Global