Reinforcement Learning and Modeling Techniques: A Review
Hindreen Rashid Abdulqadir and
Adnan Abdulazeez
Additional contact information
Hindreen Rashid Abdulqadir: Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok Kurdistan Region, Iraq.
International Journal of Science and Business, 2021, vol. 5, issue 3, 174-189
Abstract:
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL has achieved a new, complete standard of public opinion. High difficulty in large-scale real-world implementations is the effective use of large data sets previously obtained in augmented learning algorithms. Q-learning (QL), by learning a conservative Q function that allows a policy to be below the predicted value of the Q function, is introduced by us, which aims to circumvent these restrictions. We revealed technical reinforcement learning in this study. In principle, we demonstrate that QL creates a lower relation to current policy importance and that this can be correlated with guarantees of political learning theoretical change. In reality, QL strengthens the benchmark objective with a simple, standardized Q value which, in addition to existing Q-learning and essential applications, is quickly applied. The findings indicate that all algorithms are needed to learn how to play successfully. In comparison, all dual Q-learning variables have a significantly higher score compared with Q-learning, and the incremental reward function shows no improved effects than the normal reward function. We present an attack mechanism that uses the portability of competing tests to execute policy incentives and to prove their usefulness and consequences by means of a pilot study of a play learning scenario.
Keywords: Machine learning; Reinforcement learning; Modelling – Technique; Q- learning (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
https://ijsab.com/wp-content/uploads/696.pdf (application/pdf)
https://ijsab.com/volume-5-issue-3/3735 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:aif:journl:v:5:y:2021:i:3:p:174-189
Access Statistics for this article
International Journal of Science and Business is currently edited by Dr. Md Shamim Hossain
More articles in International Journal of Science and Business from IJSAB International
Bibliographic data for series maintained by Farjana Rahman (office@ijsab.com).