most citedHierarchical Solution of Markov Decision Processes using Macro-actions

226 citations · 240 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2013226 cited

Hierarchical Solution of Markov Decision Processes using Macro-actions

Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling +2

We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and…

cs.AI20134 cited

A Clustering Approach to Solving Large Stochastic Matching Problems

Milos Hauskrecht, Eli Upfal

In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The…

cs.AI20128 cited

Monte-Carlo optimizations for resource allocation problems in stochastic network systems

Milos Hauskrecht, Tomas Singliar

Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of op…

cs.LG20122 cited

Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation

Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht

In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not s…

cs.AI2012

Solving Factored MDPs with Continuous and Discrete Variables

Carlos E. Guestrin, Milos Hauskrecht, Branislav Kveton

Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods c…