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NIPS
2007
14 years 11 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
77
Voted
ICML
2005
IEEE
15 years 10 months ago
A causal approach to hierarchical decomposition of factored MDPs
We present Variable Influence Structure Analysis, an algorithm that dynamically performs hierarchical decomposition of factored Markov decision processes. Our algorithm determines...
Anders Jonsson, Andrew G. Barto
STACS
2005
Springer
15 years 3 months ago
Recursive Markov Chains, Stochastic Grammars, and Monotone Systems of Nonlinear Equations
We introduce and study Recursive Markov Chains (RMCs), which extend ordinary finite state Markov chains with the ability to invoke other Markov chains in a potentially recursive m...
Kousha Etessami, Mihalis Yannakakis
98
Voted
ICML
2006
IEEE
15 years 10 months ago
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
Mauro Maggioni, Sridhar Mahadevan
80
Voted
AAAI
1996
14 years 11 months ago
Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations
: Partially-observable Markov decision processes provide a very general model for decision-theoretic planning problems, allowing the trade-offs between various courses of actions t...
Craig Boutilier, David Poole