# Decoding Polar Codes with Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.06796 · HTML: https://mlsanity.com/paper/2009.06796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes](https://mlsanity.com/paper/2112.13934/) (2021)

## Abstract (arXiv)

In this paper we address the problem of selecting factor-graph permutations of polar codes under belief propagation (BP) decoding to significantly improve the error-correction performance of the code. In particular, we formalize the factor-graph permutation selection as the multi-armed bandit problem in reinforcement learning and propose a decoder that acts like an online-learning agent that learns to select the good factor-graph permutations during the course of decoding. We use state-of-the-art algorithms for the multi-armed bandit problem and show that for a 5G polar codes of length 128 with 64 information bits, the proposed decoder has an error-correction performance gain of around 0.125 dB at the target frame error rate of 10^{-4}, when compared to the approach that randomly selects the factor-graph permutations.