Review of Application of Model-free Reinforcement Learning in Intelligent Decision

Abstract

With the continuous development of intelligent technologies, the traditional field of decision making is gradually evolving towards Intelligent Decision (ID), but with the increasing complexity of the environment, the explosive growth of data volume and the uncertainty of the decision making process, the difficulty of decision analysis is in-creasing. As a branch of Machine Learning, Reinforcement Learning (RL) uses Agent to train and generate rewards from the environment, ultimately resulting in intelligent models. Model-free Reinforcement Learning (MFRL) is a type of reinforcement learning in which an Agent does not need a predefined model of the environment, but inter-acts directly with the environment and learns autonomously to generate optimal strategies for model generation in complex environments. Model-free Reinforcement Learning techniques applied in the field of Intelligent Decision making can improve the efficiency and accuracy of decision making in complex environments. In this paper, we provide an overview of Model-free Reinforcement Learning in intelligent decision making and introduce the basic principles of reinforcement learning and its two branches (Model-based Reinforcement Learning and Model-free Reinforcement Learning). Various algorithms of Model-free Reinforcement Learning are analyzed and disassembled from two different functions (value-based function and policy-based function), and the characteristics, applicability range, and research results of each algorithm are derived. The typical applications of Model-free Reinforcement Learning in the field of intelligent decision making are classified and analyzed. Finally, a summary and outlook on the application of Model-free Reinforcement Learning in Intelligent Decision making are presented.