arXiv AI October 2, 2026 By Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

The paper introduces a meta-multi-agent reinforcement learning (meta‑MARL) framework that enables rapid adaptation of interactive policies in multi‑agent systems. By modeling multi‑agent reinforcement learning problems as Markov games and defining a new concept called meta‑NE, the authors establish conditions linking meta‑NE to stationary points of a gradient‑play meta‑MARL algorithm. Experiments on autonomous‑driving tasks show that this approach adapts faster than pretrained MARL baselines, demonstrating its effectiveness.

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