Multi-agent Reinforcement Learning Approach to Enhance Proactive and Resilience System based IoT

Abstract

Internet of Things (IoT) systems that use reinforcement learning and multi-agent system are described in this research. Intelligent agents reside on computers that comprise this system. They enable a system to take independent actions, communicate with other systems, and adapt to changing situations. We presented a system that uses intelligent agents embedded in machines to determine which procedures are most critical and how they should be distributed. Robots with intelligent agents can improve their decision-making abilities. The proposed system and transmitting rule function compare the scheduling problem with early completion, efficiency, and delay in checking the system and the dispatching. Multi-agent using resilience systems are competitive even in a continually developing environment. It is possible that reinforcement learning with intelligence agents will be used in the future because it provides users with a unique approach to problem-solving. Additionally, these methodologies have new ways to optimize complex systems in scheduling, project planning, and other business-related domains when used in conjunction with the Internet of Things (IoT) standard technology. The rest of the paper is organized in give section, after providing the introduction, in 2nd section previous work has been discussed, in the third section, the methodology is discussed, in the 4th dataset and result and discussion is explain in the last work is concluded