Challenges and solutions for decentralized multi-agent reinforcement learning convergence
Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies
Machine LearningMultiagent Systems
Summary
Learning optimal behaviors for multiple agents working together in a network can be difficult when each agent only sees part of the system and updates using limited local information. The authors show it is generally impossible for decentralized agents to fully converge to the best joint strategies under these conditions. However, they propose a new learning method that guarantees the agents get close enough to a stable solution in a finite number of steps. They further improve convergence by adding an entropy term that encourages exploration, which reshapes the learning problem to be more manageable.
What this means in practice
- •For distributed control engineers: Design decentralized control schemes where multiple devices collaboratively optimize long-term system rewards despite partial information and local updates.
- •For robotics system integrators: Develop multi-robot teams that improve coordinated behaviors using actor-critic methods with entropy regularization to ensure reliable joint task performance.
Authors
Yizhou Zhang, Yashaswini Murthy, Laixi Shi, Adam Vierman
Abstract
We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network. Each agent in the network conducts local updates with information within its $k$-hop neighborhood and collaboratively maximizes the overall reward of the entire network. We first provide impossibility results in achieving convergence to the global optimal joint policies in our setting. These impossibility results highlight the challenges of decentralized learning with local information in multi-agent systems, namely the issues of multiagency, where each agent independently optimizes its policy, and partial observability, where agents have limited visibility into the global state. Given these challenges which indicate that decentralized policy optimization can generally be certified only up to stationarity, we provide finite-sample convergence guarantees for a decentralized actor-critic algorithm with linear function approximation that converges to an approximate stationary point with small gradient norm. To overcome such impossibility results, we further show that adding fixed-state entropy regularization reshapes the objective landscape which leads to even stronger global convergence guarantees for our proposed actor-critic algorithm.