合作多智能体强化学习中的在线变点检测

Computer Science > Multiagent Systems arXiv:2609.05298v1 (cs) [Submitted on 4 Sep 2026] Title:Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning Authors:Fatemeh Saberi Khomami, Julita Vassileva View a PDF of the paper titled Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning, by Fatemeh Saberi Khomami and 1 other authors View PDF HTML (experimental) Abstract: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 applies a statistical drift detector to flag significant shifts. We evaluate PPR in a custom Speaker-Listener environment based on the Multi-Agent Particle Environment under two controlled non-stationarity scenarios. Our results show a trade-off between detection speed and alarm stability. A smoothed-return baseline detects earlier but produces many repeated alarms. In contrast, applying the detector directly to raw returns often misses the shift. PPR offers a more balanced approach by limiting redundant detections while still identifying the controlled shifts. These findings highlight PPR as a lightweight, reward-based monitoring tool that enables cooperative MARL systems to reliably identify major changes during training. Comments: Subjects: Multiagent Systems (cs.MA); Machine Learning (cs.LG) Cite as: arXiv:2609.05298 [cs.MA] (or arXiv:2609.05298v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2609.05298 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Fatemeh Saberi Khomami [view email] [v1] Fri, 4 Sep 2026 15:49:42 UTC (746 KB) Full-text links: Access Paper: View a PDF of the paper titled Online Change-point Detection for Cooperative Multi-Agent Reinforcement Learning, by Fatemeh Saberi Khomami and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA new | recent | 2026-09 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)