Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 9 Aug 2025.
Builds on
11 of 39 references.
References · 11
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
- 2019Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learningcited by 166
- 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
- 2021Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysiscited by 26
- 2020Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channelscited by 5
- 2024Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD updatecited by 4
- 2021AI-Assisted MAC for Reconfigurable Intelligent Surface-Aided Wireless Networks: Challenges and Opportunitiescited by 3
- 2021Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learningcited by 2
- 2020A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layercited by 1
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Citing papers per year: 2026: 3.
3 papers.
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- 2025Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G
- 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
- 2024Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
- 2025Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
- 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
- 2026Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints