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Why It Matters
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.
Provenance
Discovered via ArXiv and published by ArXiv.
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Original description
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...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.05298v1 · Indexed 6 days ago