{"slug": "meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with", "title": "Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving", "summary": "A new arXiv paper (arXiv:2610.00705v1) develops a meta-multi-agent reinforcement learning (meta-MARL) framework that models multi-agent reinforcement learning problems as Markov games and enables rapid interactive policy adaptation across a distribution of Markov games. The paper defines a new solution concept, meta-NE, and establishes sufficient conditions for its equivalence to a stationary point of the gradient-play-based meta-MARL algorithm. Evaluation on autonomous-driving tasks shows the proposed meta-MARL method adapts faster than pretrained MARL baselines.", "body_md": "arXiv:2610.00705v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with", "canonical_source": "https://www.machinebrief.com/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-r8i9", "published_at": "2026-10-02 04:00:00+00:00", "updated_at": "2026-10-02 06:15:31.654228+00:00", "lang": "en", "topics": ["machine-learning", "autonomous-vehicles", "ai-research", "artificial-intelligence"], "entities": ["arXiv", "meta-MARL", "Markov games", "meta-NE"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with", "markdown": "https://wpnews.pro/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with.md", "text": "https://wpnews.pro/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with.txt", "jsonld": "https://wpnews.pro/news/meta-multi-agent-reinforcement-learning-for-fast-adaptation-of-interactive-with.jsonld"}}