Abstract
This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing meta-RL generally focuses on single-agent systems. Extending these frameworks and algorithms to multi-agent systems poses additional challenges, as tasks are characterized by not only the environment but also agents' strategic interactions. To address these challenges, we model multi-agent reinforcement learning (MARL) problems as Markov games (MGs) and develop a meta-MARL framework for rapid interactive policy adaptation across a distribution of MGs. A new concept, called meta-NE, is defined to describe the desired solution concept in a meta-MARL problem. Sufficient conditions for the equivalence between a meta-NE and a stationary point of the gradient-play-based meta-MARL algorithm are established. Our evaluation on autonomous-driving tasks demonstrates that the proposed meta-MARL method achieves faster adaptation than pretrained MARL baselines, validating the effectiveness of our framework.
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Publication details
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- Green open access
Cite this article
APA 7
Yan, H., Vamvoudakis, K. G., & Liu, M. (2026). Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving. https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving
MLA 9
Yan, Huiwen, et al. "Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving." https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving.
Chicago (author–date)
Yan, Huiwen, Kyriakos G. Vamvoudakis, and Mushuang Liu. 2026. "Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving." https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving.
Harvard
Yan, H., Vamvoudakis, K. G. and Liu, M. (2026) 'Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving', Available at: https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving.
Vancouver
Yan H, Vamvoudakis KG, Liu M. Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving. https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving
IEEE
H. Yan, K. G. Vamvoudakis, and M. Liu, "Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving," https://omanscience.com/en/articles/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-policies-with-applications-to-autonomous-driving.