The combination of Generative AI and Reinforcement Learning (RL) has made the realm of autonomous systems exciting because it considers complementary methods of enabling a real-time decision-making process. Generative AI is ideal for realistic data and simulation; however, RL defines how to learn actions in the environment by engaging in them. They let the autonomous systems predict further stances, manipulate complex conditions, and learn with the state of affairs in the environments always in place. This paper introduces the reader to Generative AI and RL and its application to autonomous systems. It will also discuss real-life examples of using Generative AI and RL in the automotive industry, industrial robots, medicine, energy, and logistics. The discussion also covers the potential changes in the technologies and how they may go further, including real-time learning at the edge, ethical interest controversies, and simulation or algorithmic computations improvements. The combination of Generative AI and Reinforcement Learning is a jump to the next level in achieving higher levels of self-organizing autonomously working systems, especially in areas of application where high levels of performance, flexibility, and robustness are requested.
Generative AI, Reinforcement Learning (RL), Autonomous Systems, Real-time Adaptation, Predictive Modeling, Industrial Robotics, Autonomous Vehicles
IRE Journals:
Kodamasimham Krishna , Aditya Mehra , Mithun Sarker , Lalit Mishra
"Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation" Iconic Research And Engineering Journals Volume 6 Issue 8 2023 Page 268-278
IEEE:
Kodamasimham Krishna , Aditya Mehra , Mithun Sarker , Lalit Mishra
"Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation" Iconic Research And Engineering Journals, 6(8)