Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
On the map: End-to-end autonomous driving → Reinforcement learning for driving also fits: Cooperative and V2X driving
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 14 of its 22 references that are in the map. First posted on arXiv 30 Sep 2026.
Builds on
14 of 22 references.
Closest ancestors · 2
Same direction, newest first
- 2026Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving19 Mar
- 2023Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors8 May
Other references · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 372
- 2019VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learningcited by 210
- 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 139
- 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
- 2021Global Convergence of Multi-Agent Policy Gradient in Markov Potential Gamescited by 89
- 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 85
- 2020A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learningcited by 40
- 2021Gradient play in stochastic games: stationary points, convergence, and sample complexitycited by 32
- 2022Potential Game-Based Decision-Making for Autonomous Drivingcited by 9
- 2022Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approachcited by 3
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- 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
- 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
- 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
- 2025Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
- 2025MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems