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AAAI
2010
14 years 18 days ago
Compressing POMDPs Using Locality Preserving Non-Negative Matrix Factorization
Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
Georgios Theocharous, Sridhar Mahadevan
ICMLA
2008
14 years 18 days ago
Prediction-Directed Compression of POMDPs
High dimensionality of belief space in Partially Observable Markov Decision Processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. ...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...
FLAIRS
2006
14 years 16 days ago
Stochastic Deliberation Scheduling using GSMDPs
We propose a new decision-theoretic approach for solving execution-time deliberation scheduling problems using recent advances in Generalized Semi-Markov Decision Processes (GSMDP...
Kurt D. Krebsbach
IJCAI
2001
14 years 16 days ago
Symbolic Dynamic Programming for First-Order MDPs
We present a dynamic programming approach for the solution of first-order Markov decisions processes. This technique uses an MDP whose dynamics is represented in a variant of the ...
Craig Boutilier, Raymond Reiter, Bob Price
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 10 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh