The general design of the automation for multiple fields using reinforcement learning algorithm
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...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-01-01.
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| Online Access: | Get fulltext |
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| LEADER | 02347 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25255_15936 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Radha, Vijaya Kumar Reddy |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Lakshmipathi, Anantha N. |e author |
| 700 | 1 | 0 | |a Tirandasu, Ravi Kumar |e author |
| 700 | 1 | 0 | |a Prakash, Paruchuri Ravi |e author |
| 245 | 0 | 0 | |a The general design of the automation for multiple fields using reinforcement learning algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-01-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25255 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a AutoML; Computational graphs; Loss function; Recurrent neural network; Reinforcement learning; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 1: January 2022; 481-487 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25255/15936 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25255/15936 |z Get fulltext |