Data-driven schemes for resolving misspecified MDPs: Asymptotics and error analysis

We consider the solution of a finite-state infinite horizon Markov Decision Process (MDP) in which both the transition matrix and the cost function are misspecified, the latter in a parametric sense. We consider a data-driven regime in which the learning problem is a stochastic convex optimization problem that resolves misspecification. Via such a framework, we make the following contributions: (1) We first show that a misspecified value iteration scheme converges almost surely to its true counterpart and the mean-squared error after K iterations is O(1/K^1/2-α) with 0 <; α <; 1/2; (2) An analogous asymptotic almost-sure convergence statement is provided for misspecified policy iteration; and (3) Finally, we present a constant steplength misspecified Q-learning scheme and show that a suitable error metric is O(1/K^1/2-α) + O(√δ) with 0 <; α <; 1/2 after K iterations where δ is a bound on the steplength.