# Construction of Polar Codes with Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.09277 · HTML: https://mlsanity.com/paper/2009.09277/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach](https://mlsanity.com/paper/2207.01105/) (2022)
- [Nested Construction of Polar Codes via Transformers](https://mlsanity.com/paper/2401.17188/) (2024)
- [A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes](https://mlsanity.com/paper/2110.01563/) (2021)
- [RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes](https://mlsanity.com/paper/2112.13934/) (2021)
- [DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning](https://mlsanity.com/paper/2402.08864/) (2024)
- [Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes](https://mlsanity.com/paper/2105.04922/) (2021)
- [A Reinforcement Learning Based Universal Sequence Design for Polar Codes](https://mlsanity.com/paper/2601.20118/) (2026)

## Abstract (arXiv)

This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game, which can be solved by reinforcement learning techniques. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability. Instead, this technique decides whether the input bits should be frozen in a purely sequential manner. The equivalence of optimizing the polar-code construction for the SCL decoder under this technique and maximizing the expected reward of traversing a maze is drawn. Simulation results show that the standard polar-code constructions that are designed for the successive-cancellation decoder are no longer optimal for the SCL decoder with respect to the frame error rate. In contrast, the simulations show that, with a reasonable amount of training, the game-based construction method finds code constructions that have lower frame-error rate for various code lengths and decoders compared to standard constructions.