Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
This work addresses the challenge of optimizing random access networks for wireless devices, offering a decentralized solution that reduces reliance on centralized training and communication overhead, though it is incremental in the context of multi-agent reinforcement learning methods.
The paper tackled the problem of designing an effective random access medium access control protocol to minimize collisions and ensure fairness in wireless networks, achieving significant performance improvements over baselines through a fully decentralized multi-agent reinforcement learning approach with consensus-based information exchange.
With wireless devices increasingly forming a unified smart network for seamless, user-friendly operations, random access (RA) medium access control (MAC) design is considered a key solution for handling unpredictable data traffic from multiple terminals. However, it remains challenging to design an effective RA-based MAC protocol to minimize collisions and ensure transmission fairness across the devices. While existing multi-agent reinforcement learning (MARL) approaches with centralized training and decentralized execution (CTDE) have been proposed to optimize RA performance, their reliance on centralized training and the significant overhead required for information collection can make real-world applications unrealistic. In this work, we adopt a fully decentralized MARL architecture, where policy learning does not rely on centralized tasks but leverages consensus-based information exchanges across devices. We design our MARL algorithm over an actor-critic (AC) network and propose exchanging only local rewards to minimize communication overhead. Furthermore, we provide a theoretical proof of global convergence for our approach. Numerical experiments show that our proposed MARL algorithm can significantly improve RA network performance compared to other baselines.