Action-Constrained Markov Decision Processes With Kullback-Leibler Cost
Abstract
This paper concerns computation of optimal policies in which the one-step reward function contains a cost term that models Kullback-Leibler divergence with respect to nominal dynamics. This technique was introduced by Todorov in 2007, where it was shown under general conditions that the solution to the average-reward optimality equations reduce to a simple eigenvector problem. Since then many authors have sought to apply this technique to control problems and models of bounded rationality in economics. A crucial assumption is that the input process is essentially unconstrained. For example, if the nominal dynamics include randomness from nature (e.g., the impact of wind on a moving vehicle), then the optimal control solution does not respect the exogenous nature of this disturbance. This paper introduces a technique to solve a more general class of action-constrained MDPs. The main idea is to solve an entire parameterized family of MDPs, in which the parameter is a scalar weighting the one-step reward function. The approach is new and practical even in the original unconstrained formulation.
Cite
@article{arxiv.1807.10244,
title = {Action-Constrained Markov Decision Processes With Kullback-Leibler Cost},
author = {Ana Bušić and Sean Meyn},
journal= {arXiv preprint arXiv:1807.10244},
year = {2018}
}