A Systematic Literature Review of Reinforcement Algorithms in Machine Learning

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.