Sliding Window Codes: Near-Optimality and Q-Learning for Zero-Delay Coding

Abstract

We study the problem of zero-delay coding for the transmission of a Markov source over a noisy channel with feedback and present a reinforcement learning solution which is guaranteed to achieve near-optimality. To this end, we formulate the problem as a Markov decision process (MDP) where the state is a probability-measure valued predictor/belief and the actions are quantizer maps. This MDP formulation has been used to show the optimality of certain classes of encoder policies in prior work, but their computation is prohibitively complex due to the uncountable nature of the constructed state space and the lack of minorization or strong ergodicity results. These challenges invite rigorous reinforcement learning methods, which entail several open questions: can we approximate this MDP with a finite-state one with some performance guarantee? Can we ensure convergence of a reinforcement learning algorithm for this approximate MDP? What regularity assumptions are required for the above to hold? We address these questions as follows: we present an approximation of the belief MDP using a sliding finite window of channel outputs and quantizers. Under an appropriate notion of predictor stability, we show that policies based on this finite window are near-optimal, in the sense that the lowest distortion achievable by such a policy approaches the true lowest distortion as the window length increases. We give sufficient conditions for predictor stability to hold. Finally, we propose a Q-learning algorithm which provably converges to a near-optimal policy and provide a detailed comparison of~the sliding finite window scheme with another approximation scheme which quantizes the belief MDP in a nearest neighbor fashion.