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 utilizes bi-level optimization to enable rapid adaptation of interactive policies in multi-agent systems, specifically applied to autonomous driving. This approach defines a meta-Nash equilibrium (meta-NE) as the solution concept, ensuring no agent can improve its expected post-adaptation return by unilaterally changing its initialization policy.

The framework employs a MAML-style algorithm where the inner loop solves Markov games to obtain task-specific policies, and the outer loop optimizes the policy initialization for fast convergence on new tasks. Evaluations on autonomous highway forced-merging scenarios demonstrate that this method achieves faster adaptation and superior safety, travel efficiency, and energy efficiency compared to pretrained MARL baselines.

The material presents a meta-learning approach to multi-agent reinforcement learning (MARL) aimed at policies that can adapt quickly when interacting with new or nonstationary agents. Its central setting is interactive decision-making, with particular relevance to autonomous driving, where an ego policy must respond to a changing population of other agents—such as different driving styles, traffic regimes, or adversarial behaviors. Rather than training a single fixed MARL policy and expecting it to generalize across all such conditions, the work frames adaptation as a bi-level optimization problem: an outer meta-learning level learns parameters or policy initializations that are broadly adaptable, while an inner level performs fast fine-tuning on a new task, scenario, or set of co-agents.

The key contribution is a meta-MARL framework that makes rapid adaptation a first-class objective in multi-agent policy learning. By using bi-level optimization, the method separates what the policy should learn in advance from what it should adjust after deployment. This is especially useful in interactive settings, where the environment is not only stochastic but also shaped by the policies of other agents, making standard single-agent meta-learning assumptions insufficient. The framework is positioned to improve sample efficiency and robustness when the agent encounters previously unseen interaction patterns, reducing the need for extensive retraining while still allowing behavior to be specialized to local conditions.

This matters because many real-world autonomous systems, including self-driving vehicles, operate in open-ended multi-agent environments where full retraining is impractical and static policies can fail under distribution shift. A meta-MARL method that supports fast adaptation could help bridge the gap between offline training and online deployment, enabling vehicles or other embodied agents to recalibrate their interactive behavior with limited new experience. More broadly, the work connects meta-learning, MARL, and safety-critical adaptation, offering a principled route toward policies that remain effective across diverse and evolving multi-agent ecosystems.