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ML
2002
ACM
143views Machine Learning» more  ML 2002»
13 years 7 months ago
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
MFCS
2010
Springer
13 years 6 months ago
Qualitative Analysis of Partially-Observable Markov Decision Processes
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observationbased strategy relies on partial information...
Krishnendu Chatterjee, Laurent Doyen, Thomas A. He...
TALG
2010
73views more  TALG 2010»
13 years 6 months ago
Discounted deterministic Markov decision processes and discounted all-pairs shortest paths
We present two new algorithms for finding optimal strategies for discounted, infinite-horizon, Deterministic Markov Decision Processes (DMDP). The first one is an adaptation of...
Omid Madani, Mikkel Thorup, Uri Zwick
AAAI
1997
13 years 9 months ago
Model Minimization in Markov Decision Processes
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for so...
Thomas Dean, Robert Givan
SODA
2010
ACM
190views Algorithms» more  SODA 2010»
14 years 5 months ago
One-Counter Markov Decision Processes
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...