Reinforcement Learning in Real-World Applications Solving Complex Problems Through Interaction and Feedback

https://doi.org/10.5281/zenodo.18062612

Abstract

Reinforcement Learning (RL) is a potent machine learning paradigm where a learning agent learns how to behave optimally by interacting with a new environment in the form of rewards. Unlike the supervised learning model, RL is not based on labeled datasets but on the decision-making in sequence under uncertainty. The research paper will look into theory behind the reinforcement learning, important algorithms and practical application in robotics, healthcare, smart grids, traffic systems and recommendation systems. The convergence of learning, improvement of policy, and better performance is proven by experimental analysis with six graphical results instead of random decision-making strategies.