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IROS
2009
IEEE
206views Robotics» more  IROS 2009»
14 years 9 days ago
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Patrick Dallaire, Camille Besse, Stéphane R...
ICML
2010
IEEE
13 years 6 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
CORR
2011
Springer
175views Education» more  CORR 2011»
13 years 19 days ago
Adaptive Channel Recommendation for Dynamic Spectrum Access
—We propose a dynamic spectrum access scheme where secondary users recommend “good” channels to each other and access accordingly. We formulate the problem as an average rewa...
Xu Chen, Jianwei Huang, Husheng Li
ICML
1996
IEEE
13 years 9 months ago
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The eval...
Rémi Munos
CCE
2004
13 years 5 months ago
Continuous reformulations of discrete-continuous optimization problems
This paper treats the solution of nonlinear optimization problems involving discrete decision variables, also known as generalized disjunctive programming (GDP) or mixed-integer n...
Oliver Stein, Jan Oldenburg, Wolfgang Marquardt