Reinforcement Learning for Jointly Optimal Coding and Control Policies for a Controlled Markovian System over a Communication Channel

We study the problem of joint optimization involving coding and control policies for a controlled Markovian system over a finite-rate noiseless communication channel. While structural results on the optimal encoding and control have been obtained in the literature, their implementation has been prohibitive in general, except for linear models. To this end, we first develop existence, regularity, and structural properties on optimal policies, followed by rigorous approximations and reinforcement