Optimally Facing the uncertainty : A brief survey on Reinforcement Learning

Authors

  • Rajeswari RR Department of Computer Science, Mother Teresa Women’s University, Kodaikanal
  • Pethalakshmi A Department of Computer Science, Mother Teresa Women’s University, Kodaikanal

Keywords:

multiarmed Bandit problem, Reinforcement Learning, Greedy algorithm

Abstract

Reinforcement Learning is a combination of supervised learning and unsupervised learning, the two main streams of Machine Learning .It has many applications in Artificial Intelligence arena. Multi Armed Bandits problem, a classical Reinforcement Learning task employs exploration and exploitation tradeoff. Efficient Bandit Algorithms for solving Bandit problem proides solutions for various problems from Dynamic pricing to online multi class prediction. This research article analyses the elements of Reinforcement learning, mathematical formulation of multi armed Bandits problem and attempts to present a naive RL algorithm for N-Queens problem for an instance of N=4 and concludes with applications of Reinforcement Learning

References

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Kakade et al, Efficient Bandit Algorithms for online multiclass prediction, Conference Proceedings, University of Pennysylvnia,2008.

Nahum Shimkin, Reinforcement Learning –Basic algorithms, Learning in Complex Systems Lecture notes, Spring 2011.

Prathamesh, Multi armed bandit approach for Dynamic pricing, M.Tech Dissertation, Iit, Mumbai, 2015

Sutton and Barto, Reinforcement Learning – an introduction, The MIT Press, England, 2017.

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http://cs.mcgill.ca/~dprecup/courses /AI/Lectures/ai-lecture13.pdf

www.medium.com/machinelearning for humans .html

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Published

2025-11-13

How to Cite

[1]
R. R. Rajeswari and A. Pethalakshmi, “Optimally Facing the uncertainty : A brief survey on Reinforcement Learning”, Int. J. Comp. Sci. Eng., vol. 6, no. 4, pp. 45–48, Nov. 2025.

Issue

Vol. 6 No. 4 (2018): IJCSE Special Issue May Edition

Section

Survey Article

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