mlsanityData to 14 Oct 2026

Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes

arXiv 2507.17893 ↗First posted 23 Jul 2025cs.IT cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

This paper explores the application of reinforcement learning techniques to enhance the performance of decoding of linear block codes based on flipping bits and finding optimal decisions. We describe the methodology for mapping the iterative decoding process into Markov Decision Processes (MDPs) and propose different methods to reduce the number of states in the MDP. A truncated MDP is proposed to reduce the number of states in the MDP by learning a Hamming ball with a specified radius around codewords. We then propose a general scheme for reinforcement learning based decoders applicable to any class of codes to improve the performance of decoders. We call this scheme an action-list decoding. 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. Additionally, we propose a feedback-based method to exploit and enhance the performance of existing high-performing decoders by applying reinforcement learning algorithms after the existing decoders. These approaches effectively reduces the complexity of the reinforcement learning block. Finally, we present experimental results for the Low-Density Parity Check (LDPC) codes over the Binary Symmetric Channel (BSC) to demonstrate the efficiency of the proposed methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 21 references that are in the map. First posted on arXiv 23 Jul 2025.

Builds on

7 of 21 references.

References · 7

  1. 2024Reinforcement learningcited by 247
  2. 2022Simulation-guided Beam Search for Neural Combinatorial Optimizationcited by 84
  3. 2016Learning to Decode Linear Codes Using Deep Learningcited by 61
  4. 2018Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approachcited by 34
  5. 2017Neural Offset Min-Sum Decodingcited by 24
  6. 2019Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decodingcited by 10
  7. 2023Generalization Bounds for Neural Belief Propagation Decoderscited by 4

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  1. 2024GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach
  2. 2026ZeroCode: On-demand Error-Correcting Code Construction from the Zero Matrix via Reinforcement Learning
  3. 2026Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
  4. 2025Learning Linear Block Codes with Gradient Quantization
  5. 2026A Reinforcement Learning Based Universal Sequence Design for Polar Codes
  6. 2025Discovering highly efficient low-weight quantum error-correcting codes with reinforcement learning
  7. 2026Adaptive Decoding via Test-Time Policy Learning for Self-Improving Generation
  8. 2025Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction