Meta reinforcement learning speeds up adaptation in multi-agent driving systems
Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving
Artificial IntelligenceMultiagent Systems
Summary
When multiple AI agents interact, like self-driving cars on the road, they need to quickly learn and adapt to each other’s behaviors to act safely. The authors developed a new learning method that helps these agents adjust much faster to changing driving scenarios compared to older methods. They treat the interactions among agents as a kind of game and teach the agents to adapt their strategies quickly across different game situations. Tests show their approach works better in autonomous driving tasks, helping cars respond smarter and faster to others.
What this means in practice
- •For autonomous vehicle engineers: Improve self-driving cars' ability to rapidly adapt their driving strategies when encountering new drivers and road conditions using interactive multi-agent learning.
- •For robotic fleet operators: Enable fleets of robots to quickly adjust coordinated behaviors in dynamic environments by applying fast-adapting multi-agent reinforcement learning methods.
Authors
Huiwen Yan, Kyriakos G. Vamvoudakis, Mushuang Liu
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.