Off-line Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity

In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an off-line setting with a fixed data set of size m, collected using a so-called logging policy. We develop sample complexity bounds for the estimator and establish conditions for minimaxity. Our statistical bounds depend on the logging policy through its mixing properties. We show that achieving a particular statistical risk bound involves a subtle and interesting trade-off between the strength of the mixing properties and the number of samples. We demonstrate the validity of our results under various examples, such as ergodic Markov chains; weakly ergodic inhomogeneous Markov chains; and controlled Markov chains with nonstationary Markov, episodic, and greedy controls. Lastly, we use these sample complexity bounds to establish concomitant ones for off-line evaluation of stationary Markov control policies.

Funding: I. Banerjee was supported in part by the Ross-Lynn fellowship and McLean scholarship at Purdue University. H. Honnappa was partly supported by the National Science Foundation [Grants CAREER/2143752, DMS/1812197 and DMS/2153915]. V. Rao was supported by the National Science Foundation [Grants RI/1816499 and DMS/1812197].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.0046.