Reinforcement learning based multi-agent system for smart microgrid

Smart microgrid (SMG) communication networks face significant challenges in maintaining high Quality of Service (QoS) due to dynamic load variations, fluctuating network conditions, and potential component faults, which can increase latency, reduce throughput, and compromise fault recovery. The growing integration of distributed renewable energy resources demands adaptive and intelligent routing mechanisms capable of operating efficiently under such diverse and fault-prone conditions. This paper presents a Q-Reinforcement Learning-based Multi-Agent Bellman Routing (QRL-MABR) algorithm, which enhances the traditional MABR approach by embedding a Q-learning module within each network agent. Agents dynamically learn optimal routing policies, balance exploration and exploitation action selection with adaptive temperature scaling, and jointly optimize latency, throughput, jitter, convergence speed, and fault resilience.

Simulations on IEEE 9, 14, 34, 39, and 57 bus SMG testbeds demonstrate that QRL-MABR significantly outperforms conventional routing protocols (MABR, RIP, OLSR, OSPFv2) and advanced RL-based algorithms (SN-MAPPO, DDQL, MDDPG, SARSA-$\lambda$, TD3), achieving 16%–28% delay reduction, 14%–16% throughput gains, 17%–21% jitter improvement, and superior fault recovery. Thus, QRL-MABR provides a robust, scalable, and intelligent framework for next-generation smart microgrids.