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Research Article
Optimization of Ads Using Reinforcement Learning and Comparison of Algorithms
Sharan Nishanth.M1
Giridharadhayalan.m2
Karthi Raja.S3
Yuvaraj.E4
2Asst. Professor, Department of Mechatronics, Dr. Mahalingam College of Engineering and Technology, Pollachi,Tamilnadu, India. 134 Department of Mechatronics, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India.
Published Online: May-June 2023
Pages: 115-118
Cite this article
↗ 10.59256/ijire.2023040362
References
[1]. Kuleshov, V., &Precup, D. (2014). Algorithms for multi-armed bandit problems. Journal of Machine Learning Research, 15(1), 95-
127.
[2]. Lattimore, T., &Szepesvári, C. (2020). Bandit algorithms (Vol. 1). Cambridge University Press
[3]. Chapelle, O., & Li, L. (2011). An empirical evaluation of Thompson sampling. In Advances in neural information processing systems
(pp. 2249-2257).
[4]. May, A., &Korda, N. (2012). Thompson sampling for 1-dimensional exponential families with unknown variance. In Advances in
Neural Information Processing Systems (pp. 3034-3042).
[5]. Russo, D. (2016). A Tutorial on Thompson Sampling. Foundations and Trends® in Machine Learning, 9(4-5), 338-403
127.
[2]. Lattimore, T., &Szepesvári, C. (2020). Bandit algorithms (Vol. 1). Cambridge University Press
[3]. Chapelle, O., & Li, L. (2011). An empirical evaluation of Thompson sampling. In Advances in neural information processing systems
(pp. 2249-2257).
[4]. May, A., &Korda, N. (2012). Thompson sampling for 1-dimensional exponential families with unknown variance. In Advances in
Neural Information Processing Systems (pp. 3034-3042).
[5]. Russo, D. (2016). A Tutorial on Thompson Sampling. Foundations and Trends® in Machine Learning, 9(4-5), 338-403
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