Action-List Reinforcement Learning Decoders

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 benefit of automorphism group of code to further improve the code performance. Finally, we present experimental results for the Binary Symmetric Channel (BSC) to demonstrate the efficiency of the proposed methods.