157 citations · 540 across the 7 of their papers we have counts for
7 papers
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
Ron Parr
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these appr…
Policy Iteration for Factored MDPs
Daphne Koller, Ron Parr
Many large MDPs can be represented compactly using a dynamic Bayesian network. Although the structure of the value function does not retain the structure of the process, recent wor…
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
Uri Lerner, Ron Parr
An important subclass of hybrid Bayesian networks are those that represent Conditional Linear Gaussian (CLG) distributions --- a distribution with a multivariate Gaussian component…
Value Function Approximation in Zero-Sum Markov Games
Michail Lagoudakis, Ron Parr
This paper investigates value function approximation in the context of zero-sum Markov games, which can be viewed as a generalization of the Markov decision process (MDP) framework…
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
Gavin Taylor, Ron Parr
Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear Programming (RALP) could produce value functions and policies which compared favorably to established lin…
Efficient Selection of Disambiguating Actions for Stereo Vision
Monika Schaeffer, Ron Parr
In many domains that involve the use of sensors, such as robotics or sensor networks, there are opportunities to use some form of active sensing to disambiguate data from noisy or…