Weighted mesh algorithms for general Markov decision processes: Convergence and tractability
This provides a method for efficiently solving MDPs in control and optimization, though it is incremental as it builds on existing mesh and randomization approaches.
The authors tackled the problem of solving discrete-time, finite-horizon Markov Decision Processes (MDPs) with general state and action spaces, achieving tractable computational complexity polynomial in the time horizon for bounded spaces and semi-tractable complexity for unbounded spaces.
We introduce a mesh-type approach for tackling discrete-time, finite-horizon Markov Decision Processes (MDPs) characterized by state and action spaces that are general, encompassing both finite and infinite (yet suitably regular) subsets of Euclidean space. In particular, for bounded state and action spaces, our algorithm achieves a computational complexity that is tractable in the sense of Novak and Wozniakowski, and is polynomial in the time horizon. For unbounded state space the algorithm is "semi-tractable" in the sense that the complexity is proportional to $ε^{-c}$ with some dimension independent $c\geq2$, for achieving an accuracy $ε$, and polynomial in the time horizon with degree linear in the underlying dimension. As such the proposed approach has some flavor of the randomization method by Rust which deals with infinite horizon MDPs and uniform sampling in compact state space. However, the present approach is essentially different due to the finite horizon and a simulation procedure due to general transition distributions, and more general in the sense that it encompasses unbounded state space. To demonstrate the effectiveness of our algorithm, we provide illustrations based on Linear-Quadratic Gaussian (LQG) control problems.