TY - CONF N1 - cited By 1; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661 AV - none TI - Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning Y1 - 2021/// KW - Decision making; Electric power transmission networks; Network layers; Network routing; Quality of service; Reinforcement learning; Reliability; Smart power grids KW - Agent based; Bellman-Ford; Microgrid; Multi agent; Network faults; Network reliability; Reinforcement learnings; Routings; Smart grid; Smart Micro Grids KW - Multi agent systems SN - 9781728176666 PB - Institute of Electrical and Electronics Engineers Inc. A1 - Singh, N. A1 - Elamvazuthi, I. A1 - Nallagownden, P. A1 - Badruddin, N. A1 - Ousta, F. A1 - Jangra, A. UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b N2 - A Smart Microgrid consists of physical and communication layered networks. It provides communication services to each connected component and resource through multi-agent system. This paper proposes a reinforcement learning based methodology, Q-reinforcement Learning based Multi-agent based Bellmanford Routing (QRL-MABR), using multiple agents communicating over the microgrid network. It strengthens the decision-making core of the microgrid by improving Quality of service and network reliability of the smart microgrid. The performance analysis of the algorithm is tested over small-scale IEEE microgrid models i.e. IEEE 9 and IEEE 14. The work is tested and compared with four routing oriented decision-making algorithms, Open shortest path first (OSPF), Optimized link state routing (OLSR), Routing information protocol (RIP) and Multi-agent based Bellmanford routing (MABR). The results validate the productivity and learning capabilities of the proposed QRL-MABR algorithm. © 2021 IEEE. ID - scholars15491 ER -