REINFORCEMENT LEARNING: FRAMEWORK, APPLICATIONS AND CHALLENGES

Abstract

Artificial Intelligence is the science of developing intelligent machines that can simulate human intelligence and perform complex tasks. Reinforcement learning (RL) is the trending interdisciplinary learning paradigm of machine learning and optimal control that mirrors the hit and trial learning mechanism of humans .The self-learning agents take actions in a dynamic environment, get rewarded or punished and use this feedback to learn optimal decision-making to maximize cumulative rewards over time. This objective of this paper is to present an insight into promising future technology. The introduction to the technology is followed by the key concepts that constitute the reinforcement framework, the different models of this paradigm, the applications that span a wide arena and the challenges facing this ever-evolving strategy.