The DOI matches its Crossref record; 419 of the 419 references it cites were found in a registry or an index.
A Gentle Introduction to Reinforcement Learning and its Application in Different Fields
Open accessAbstract
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Cited by
Akinola, O., Akinola, A., Victor Ifeanyi, I., Oyerinde, O., Joseph Adewole, O., Sulaimon, B., & Sulaimon, B. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24mar1231 · Cited by 283
Deep reinforcement learning in production systems: a systematic literature review
Panzer, M., & Bender, B. (2022) · International Journal of Production Research · doi:10.1080/00207543.2021.1973138 · Cited by 282
Artificial intelligence in pancreatic cancer
Huang, B., Huang, H., Zhang, S., Zhang, D., Shi, Q., Liu, J., & Guo, J. (2022) · Theranostics · doi:10.7150/thno.77949 · Cited by 142
“Then, it uses this feedback to train itself and collect experience and knowledge about the environment to achieve specific goals [66].”
Method
Metadata
- Authors
- Naeem, M., Rizvi, S. T. H., & Coronato, A.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 209320-209344
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3099098707
- Cited by
- 257
- References
- 419
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- gold
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