Markov model

Scheduling (production processes)

Mathematics

Software agent

Reinforcement learning

Markov chain

Human–computer interaction

Distributed computing

Statistics

Markov decision process

Variety (cybernetics)

Machine learning

Engineering

Artificial intelligence

Markov process

Partially observable Markov decision process

Programming language

Operations management

Software

Computer science

Process (computing)

Robotics

Robot

IEEE Access

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields