We describe a fast multiscale procedure for repeatedly compressing or homogenizing Markov decision processes (MDPs), wherein a hierarchy of sub-problems at ...
Abstract— Many problems in sequential decision making and stochastic control naturally enjoy strong multiscale structure:.
We describe a fast multiscale procedure for repeatedly compressing or homogenizing Markov decision processes (MDPs), wherein a hierarchy of sub-problems at ...
Dec 5, 2012 · We describe a fast multiscale procedure for repeatedly compressing, or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of ...
Missing: Efficient | Show results with:Efficient
We describe a fast multiscale procedure for repeatedly compressing, or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of sub-problems at ...
The recursive structure of 1-D Markov processes makes them simple to analyze, and generally leads to compu- tationally efficient algorithms for statistical ...
Dec 5, 2012 · Multiscale Markov Decision Problems levels of “abstraction” of the original problem. While automating the process of hierar- chically ...
The multiscale construction we discuss enables the efficient solution of such an equation: the inverse operator needed to solve Bellman's can be expressed ...
In this paper, we study the state and action abstraction of Markov decision processes. (MDP) from a tensor decomposition view. We focus on the batch data ...
For example, it may be effective to allocate resources to agents based on the expected value gained or lost due to changing its action from its intended course.