Convergence of Policy Iteration for Entropy-Regularized Stochastic Control Problems

For a general entropy-regularized stochastic control problem on an infinite horizon, we prove that a policy iteration algorithm (PIA) converges to an optimal relaxed control. Contrary to the standard stochastic control literature, classical H"older estimates of value functions do not ensure the convergence of the PIA, due to the added entropy-regularizing term. To circumvent this, we carry out a delicate estimation by moving back and forth between appropriate H"older and Sobolev spaces. This requires new Sobolev estimates designed specifically for the purpose of policy iteration and a nontrivial technique to contain the entropy growth. Ultimately, we obtain a uniform H"older bound for the sequence of value functions generated by the PIA, thereby achieving the desired convergence result. Characterization of the optimal value function as the unique solution to an exploratory Hamilton-Jacobi-Bellman equation comes as a by-product. The PIA is numerically implemented in an example of optimal consumption.