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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An Online Minimax Optimal Algorithm for Adversarial Multiarmed Bandit Problem.

Kaan Gokcesu, Suleyman Serdar Kozat

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    Summary

    We introduce a novel online algorithm for the adversarial multiarmed bandit problem. This algorithm achieves near-optimal performance without prior knowledge of game length or strategy switches, demonstrating significant gains in big data applications.

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    Area of Science:

    • Machine Learning
    • Reinforcement Learning
    • Online Algorithms

    Background:

    • The adversarial multiarmed bandit problem presents a significant challenge in online decision-making.
    • Existing algorithms often require knowledge of game length or strategy switches, limiting their applicability.
    • Developing truly online algorithms with strong performance guarantees is crucial for real-world applications.

    Purpose of the Study:

    • To introduce a novel online algorithm for the adversarial multiarmed bandit problem.
    • To achieve asymptotically optimal performance compared to the best switching bandit arm selection strategy.
    • To provide algorithms with performance guarantees independent of statistical assumptions on arm losses.

    Main Methods:

    • Development of a truly online algorithm for the adversarial multiarmed bandit problem.
    • Theoretical analysis to establish individual sequence performance guarantees without statistical assumptions.
    • Derivation of minimax optimal regret bounds up to logarithmic terms.
    • Achieving log-linear computational complexity with respect to game length.

    Main Results:

    • The proposed algorithm asymptotically matches the performance of the best switching bandit strategy.
    • Regret bounds are minimax optimal, holding in an individual sequence manner.
    • Computational complexity is log-linear, enabling efficient application to big data.
    • Experimental results show significant performance improvements over state-of-the-art algorithms.

    Conclusions:

    • The developed online algorithm offers a powerful solution for the adversarial multiarmed bandit problem.
    • The algorithm's efficiency and strong theoretical guarantees make it suitable for big data scenarios.
    • A general, implementable framework for bandit arm selection is introduced, adaptable to various applications.