# Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

## TL;DR

This paper introduces a reinforcement learning approach to optimize bit-flipping decoding strategies for binary linear codes, achieving improved performance and efficiency over traditional heuristics.

## Contribution

It presents a novel method of applying reinforcement learning to decode binary linear codes, replacing heuristic decisions with data-driven strategies.

## Key findings

- Learned decoders outperform traditional heuristics.
- Achieve near-optimal decoding performance in some cases.
- Faster convergence when biasing learning towards correct decisions.

## Abstract

In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bit-flipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge.