# Reinforcement Learning for Near-Optimal Design of Zero-Delay Codes for Markov Sources

arXiv: https://arxiv.org/abs/2311.12609 · HTML: https://mlsanity.com/paper/2311.12609/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Jointly Optimal Coding and Control Policies for a Controlled Markovian System over a Communication Channel](https://mlsanity.com/paper/2411.13884/) (2024)
- [Exploration-Exploitation Tradeoff in Universal Lossy Compression](https://mlsanity.com/paper/2506.20261/) (2025)
- [Reinforcement Learning for Discounted and Ergodic Control of Diffusion Processes](https://mlsanity.com/paper/2603.13155/) (2026)

## Abstract (arXiv)

In the classical lossy source coding problem, one encodes long blocks of source symbols that enables the distortion to approach the ultimate Shannon limit. Such a block-coding approach introduces large delays, which is undesirable in many delay-sensitive applications. We consider the zero-delay case, where the goal is to encode and decode a finite-alphabet Markov source without any delay. It has been shown that this problem lends itself to stochastic control techniques, which lead to existence, structural, and general structural approximation results. However, these techniques so far have resulted only in computationally prohibitive algorithmic implementations for code design. To address this problem, we present a reinforcement learning design algorithm and rigorously prove its asymptotic optimality. In particular, we show that a quantized Q-learning algorithm can be used to obtain a near-optimal coding policy for this problem. The proof builds on recent results on quantized Q-learning for weakly Feller controlled Markov chains whose application necessitates the development of supporting technical results on regularity and stability properties, and relating the optimal solutions for discounted and average cost infinite horizon criteria problems. These theoretical results are supported by simulations.