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      Retracted: The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

      retraction
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

          This study explores the application of deep reinforcement learning (DRL) in the Internet of Things (IoT) sports game design. The fundamentals of DRL are deeply understood by investigating the current state of IoT fitness applications and the most popular sports game design architectures. The research object is the ball return decision problem of the popular game of table tennis robot return. Deep deterministic policy gradients are proposed by applying DRL to the ball return decision of a table tennis robot. It mainly uses the probability distribution function to represent the optimal decision solution in the Markov Model decision process to optimize the ball return accuracy and network running time. The results show that in the central area of the table, the accuracy of returning the ball is higher, reaching 67.2654%. Different tolerance radii have different convergence curves. When r = 5 cm, the curve converges earlier. After 500,000 iterations, the curve converges, and the accuracy rate is close to 100%. When r = 2 cm and the number of iterations is 800,000, the curve begins to converge, and the accuracy rate reaches 96.9587%. When r = 1 cm, it starts to converge after 800,000 iterations, and the accuracy is close to 56.6953%. The proposed table tennis robot returns the ball in line with the requirements of the actual environment. It has practical application and reference value for developing IoT fitness and sports.
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            Author and article information

            Contributors
            Journal
            Comput Intell Neurosci
            Comput Intell Neurosci
            cin
            Computational Intelligence and Neuroscience
            Hindawi
            1687-5265
            1687-5273
            2023
            4 October 2023
            4 October 2023
            : 2023
            : 9807067
            Affiliations
            Article
            10.1155/2023/9807067
            10567329
            37829924
            01618ae7-2cc2-4403-a970-a46b48eeddb3
            Copyright © 2023 Computational Intelligence and Neuroscience.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 3 October 2023
            : 3 October 2023
            Categories
            Retraction

            Neurosciences
            Neurosciences

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