Cognitive Analytics and Reinforcement Learning

15Reinforcement Learning in Healthcare: Applications and Challenges

Tribhangin Dichpally, Yatish Wutla and Sheela Jayachandran*

Department of Computer Science and Engineering (SCOPE), VIT-AP University, Amravathi, Andhra Pradesh, India

Abstract

Reinforcement learning, also known as RL, is a decision-making approach that entails engaging with complicated settings with the goal of maximizing the long-term benefits while abiding by a predetermined policy and accepting helpful criticism for improvement. A computerized agent interacts with a novel environment, makes decisions, and eventually learns about its dynamics. It has proven advantages over other learning strategies when it comes to making medical decisions. It places a focus on long-term gains and is capable of handling difficult, lengthy sequential decision-making problems involving delayed, exhaustive, and sampled data. It has evolved into a potent strategy for creating effective healthcare solutions. In order to analyze the function of RL in healthcare, this chapter looks at earlier research, points out any flaws, and makes assumptions about potential future contributions.

Keywords: Reinforcement learning, Markov decision process, decision making, artificial intelligent, machine learning

15.1 Introduction

As a result of rapid advances in computer science and creation of sophisticated algorithms, the growing field of computing intelligence has achieved tremendous progress towards its ultimate goal. AI, which simulates ...