Open AccessThe general design of the automation for multiple fields using reinforcement learning algorithm
Author(s) -
V. Radha,
N Lakshmipathi Anantha,
Ravi Kumar Tirandasu,
Paruchuri Ravi Prakash
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i1.pp481-487
Subject(s) - reinforcement learning , computer science , artificial intelligence , machine learning , automation , graph , population , representation (politics) , set (abstract data type) , algorithm , theoretical computer science , engineering , mechanical engineering , demography , sociology , politics , law , political science , programming language
Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.