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arXiv Machine Learning By Hannes B\"uchi, Manon Flageat, Eduardo Sebasti\'an, Amanda Prorok

Events as Triggers for Behavioral Diversity in Multi-Agent Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces a new multi‑agent reinforcement learning framework that separates agent identity from behavior by using events to trigger behavioral changes. It proposes Neural Manifold Diversity (NMD) to create a continuous behavior manifold and an event‑based hypernetwork that generates Low‑Rank Adaptation (LoRA) modules for on‑the‑fly policy reconfiguration. The authors prove that this design preserves reward maximization and show empirically that it outperforms baselines, generalizes zero‑shot, and uniquely solves tasks requiring sequential behavior reassignment.

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