Reinforcement Learning Algorithms: An Overview and Classification (original) (raw)

Introduction of Reinforcement Learning and Its Application Across Different Domain

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

In the modern era of rapid development in Deep Neural Networks, Reinforcement Learning (RL) has evolved into a pivotal and transformative technology. RL, a learning process where these machine agent interacts with several unknown environment through trial and error. The agent, responsive to the learning machine, go through these interaction, and start receiving feedback in the form of positive rewards or negative rewards like penalties from the environment, and constantly refines its behavior. This research paper offers an in-depth introduction to the foundational concepts of RL, focusing on Markov Decision Processes and various RL algorithms. Machine Learning (ML) is a subset of Artificial Intelligence, which deals with ‘‘the question of how to develop software agents (Machine) that improve automatically with experience’’. The basic three categories of Machine Learning are. 1. Supervised Learning 2. Unsupervised Learning 3. Reinforcement Learning RL method is that in any situation the agent has to choose between using its acquired knowledge of the environment i.e. using an action already tried or performed previously or exploring actions never tried before in that situation. In this review paper, we will discuss the most used learning algorithms in games robotics and healthcare, autonomous control as well as communication and networking, natural language processing.[1]

Exploring Reinforcement Learning: Algorithms and Applications in Machine Learning

Zenodo (CERN European Organization for Nuclear Research), 2023

Machine learning plays a pivotal role in artificial intelligence, allowing machines to mimic human language and making tremendous progress in a wide range of fields. Machine learning has become widely popular owing to its adaptability and breadth of application. Reinforcement learning is one of the most well-known uses of machine learning; it allows robots and software agents to learn and adjust their behaviour in order to achieve better results in a given setting. As a result of its many advantages in developing intelligent agents, including selfimprovement, web-based learning, and decreased programming requirements, reinforcement learning has emerged as a leading technique in this field. Even if there is constant research to increase security and efficiency of algorithms, there is still a lot of potential for advancement. Therefore, the purpose of this study is to give a thorough examination of reinforcement learning and its applications within the larger field of Machine Learning, and it does so by making use of a wide range of algorithmic techniques.

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

IEEE Access, 2020

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.

Guest editorial: special issue on reinforcement learning for real life

Machine Learning, 2021

Reinforcement learning (RL) is a general paradigm for learning, predicting, and decision making, with broad applications in sciences, engineering and arts. RL has seen prominent successes in many problems, such as Atari games, AlphaGo, robotics, recommender systems, and AutoML. However, applying RL in the real world remains challenging. A natural question arises: What are the challenges and how to address them? The main goals of the special issue are to: (1) identify key research problems that are critical for the success of real-world applications; (2) report progress on addressing these critical issues; and (3) have practitioners share their successful stories of applying RL to real-world problems, and the insights gained from the applications. We received 60 submissions, following an open call for papers successfully applying RL algorithms to real-life problems and/or addressing practically relevant RL issues, with respect to practical RL algorithms, practical issues, and applications. After a rigorous reviewing process, we accepted 11 articles, each of which was assessed by at least three reviewers, with one, mostly two, or three rounds of revisions.

IJERT-Reinforcement Learning:Recent Threads

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/reinforcement-learning-recent-threads https://www.ijert.org/research/reinforcement-learning-recent-threads-IJERTCONV8IS04015.pdf Reinforcement learning is a method of training algorithms using reward and punishment feedback. Reinforcement learning agents will interact with their environment to extract information. It is using a trial and error mechanism to learn from its experiences. The goal of reinforcement learning is getting a model that can maximize the total aggregate reward. Its policy is similar to supervised learning. When comparing, both reinforcement learning and supervised learning uses the mapping between input and output as a policy method. This paper contains detailed comparisons and discussion of six reinforcement algorithms, their exploration and exploitation strategy, their weakness and strengths. Background on reinforcement learning models and its recent trends, advantages and future opportunities of reinforcement learning are presented in the paper. This paper is keen to discuss the state-of-the-art applications and achievements of reinforcement learning in various domains.

Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

IT journal research and development, 2024

Reinforcement Learning (RL) is fast gaining traction as a major branch of machine learning, its applications have expanded well beyond its typical usage in games. Several subfields of reinforcement learning like deep reinforcement learning and multi-agent reinforcement learning are also expanding rapidly. This paper provides an extensive review on the field from the point of view of Machine Learning (ML). It begins by providing a historical perspective on the field then proceeds to lay a theoretical background on the field. It further discusses core reinforcement learning problems and approaches taken by different subfields before discussing the state of the art in the field. An inexhaustive list of applications of reinforcement learning is provided and their practicability and scalability assessed. The paper concludes by highlighting some open areas or issues in the field.

Reinforcement Learning Rebirth, Techniques, Challenges, and Resolutions

JOIV : International Journal on Informatics Visualization, 2020

Reinforcement learning (RL) is a new propitious research space that is well-known nowadays on the internet of things (IoT), media and social sensing computing are addressing a broad and pertinent task through making decisions sequentially by deterministic and stochastic evolutions. The IoTs extend world connectivity to physical devices like electronic devices network by use interconnect with others over the Internet with the possibility of remotely being supervised and meticulous. In this paper, we comprehensively survey an in-depth assessment of RL techniques in IoT systems focusing on the main known RL techniques like artificial neural network (ANN), Q-learning, Markov Decision Process (MDP), Learning Automata (LA). This study examines and analyses learning technique with focusing on challenges, models performance, similarities and the differences in IoTs accomplish with most correlated proposed state of the art models. The results obtained can be used as a foundation for designin...

A Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

REINFORCEMENT LEARNING AND ITS APPLICATIONS

IAEME PUBLICATION, 2020

Reinforcement learning comes under the field of machine learning which one of the dominating research fields these days. It is a semi-supervised method of learning in which actions are taken to maximize the reward in a particular direction. It is a sequential method in which the next output depends on the previous input and hence a computer system intelligently decides on the basis of the experiences gained. Authors have discussed the basic methodology of reinforcement learning and the challenges faced by it. Another topic discussed herein is the applications of reinforcement learning. Some of the applications of reinforcement learning are Resources management in computer clusters, Traffic light control, Robotics, Web system configuration, Chemistry, Personalized recommendations and deep learning. Results have proved that reinforcement learning is a better method of machine learning when an automatic system need to be created. This technique helps in smart and real time learning of a system to perform according to the present situation in an experiment.

Reinforcement Learning: A Comprehensive Overview

International Journal of Innovative Research in Computer Science and Technology, 2024

Machine Learning is one of the most essential parts of Artificial Intelligence. Machine learning now exists as an important innovation and has a sufficient number of uses. Reinforcement Learning is one of the largest Machine Learning applications that enable machines and software agents to work more precisely and resolve behaviors within a specific context in order to maximize their performance. The self-improvement feature, web-based learning, and minimal effort of Strengthening Learning helped the machines become smart agents in basic technology. With the development of robust and effective algorithms, there is still a lot of work to be done. Therefore, the main purpose of this study is to provide Confirmation Learning reviews and applications using various algorithms from a machine learning perspective.

Reinforcement Learning Algorithms: An Overview and Classification (original) (raw)

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