@inproceedings{scholars15491,
           title = {Smart Microgrid QoS and Network Reliability Performance Improvement using Reinforcement Learning},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021},
             doi = {10.1109/ICIAS49414.2021.9642596},
            year = {2021},
            note = {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},
            isbn = {9781728176666},
          author = {Singh, N. and Elamvazuthi, I. and Nallagownden, P. and Badruddin, N. and Ousta, F. and Jangra, A.},
        abstract = {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. {\^A}{\copyright} 2021 IEEE.},
        keywords = {Decision making; Electric power transmission networks; Network layers; Network routing; Quality of service; Reinforcement learning; Reliability; Smart power grids, Agent based; Bellman-Ford; Microgrid; Multi agent; Network faults; Network reliability; Reinforcement learnings; Routings; Smart grid; Smart Micro Grids, Multi agent systems},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124122304&doi=10.1109\%2fICIAS49414.2021.9642596&partnerID=40&md5=9e9acf69b67deadb43bd568a208b166b}
}