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A Systematic Literature Review of Reinforcement Algorithms in Machine Learning

A Systematic Literature Review of Reinforcement Algorithms in Machine Learning
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Author(s): Gabriel Kabanda (Woxsen University, India)and Hemachandran Kannan (Woxsen University, India)
Copyright: 2023
Pages: 17
Source title: Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence
Source Author(s)/Editor(s): Kamal Kant Hiran (Aalborg University, Denmark), K. Hemachandran (Woxsen University, India), Anil Pise (University of the Witwatersrand, South Africa)and B. Justus Rabi (Christian College of Engineering and Technology, India)
DOI: 10.4018/978-1-6684-6519-6.ch002

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Abstract

Reinforcement learning (RL) is learning from interactions with the environment in order to accomplish certain long-term objectives connected to the environmental condition. Reinforcement learning takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The purpose of the research was to conduct a systematic literature review of reinforcement algorithms in machine learning in order to develop a successful multi-agent RL algorithm that can be applied to robotics, network packet routing, energy distribution, and other applications. The robotics-related RL techniques of value-based RL, policy-based RL, model-based RL, deep reinforcement learning, meta-RL, and inverse RL were examined. As a result, the robotics-related RL techniques of value-based RL, policy-based RL, model-based RL, deep RL, meta-RL, and inverse RL were discussed in this research work. The asynchronous advantage actor-critic algorithm (A3C) is one of the best reinforcement algorithms. A3C performs better on deep RLchallenges and is quicker and easier to use.

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