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.

本文使用强化学习技术，针对二元线性码设计有效的解码策略，通过将迭代解码算法映射到马尔可夫决策过程中，实现数据驱动的最优决策学习，以比特翻转解码为例研究了在对称二元信道和加性高斯白噪声通道中，重复-穆勒码和 BCH 码中得到的解码性能与复杂度之间的折衷，并表明倾向于正确解码决策的学习过程可以加速学习收敛。

通道编码的强化学习: 学习式比特翻转解码