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Creators/Authors contains: "Taghipour, Milad"

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  1. ; (, IEEE)
    This paper explores the application of reinforcement learning techniques to enhance the performance of decoding based on flipping bits and finding optimal decisions. We begin by providing an overview of bit-flipping-based decoders and reinforcement learning algorithms. We then describe the methodology for mapping the iterative decoding process into Markov Decision Processes (MDPs) and propose a general action list decoding method for reinforcement learning based decoders, irrespective of the class of codes, to improve the performance of decoders. We design an action-list decoder based on the Deep-Q network values that substantially enhance performance. We also get the benefit of the automorphism group of the code to further improve code performance. Finally, we present experimental results for the Binary Symmetric Channel (BSC) to demonstrate the efficiency of the proposed methods. 
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    Free, publicly-accessible full text available August 18, 2026
  2. ; ; (, International Foundation for Telemetering)
    Accepted Manuscript Full Text Available