Reinforcement Learning

Abstract

In this chapter, reinforcement learning (RL), a subfield of machine learning that has gained prominence because it enables agents to interact with their surroundings and learn from their mistakes, is covered in great detail. The chapter looks at the core elements of RL, including agents, actions, states, and rewards, in addition to examining a number of algorithms, including policy gradients, SARSA, and Q-learning. It also examines the difficulties and constraints of RL, such the conflict between exploration and exploitation and the instability of deep learning. Further research and development are required to realise RL's promise to transform society and technology. The chapter concludes with a list of numerous RL applications in industries like robotics, gaming, banking, and healthcare.