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» Solving Factored MDPs with Continuous and Discrete Variables
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AAAI
2012
12 years 1 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
CSDA
2008
98views more  CSDA 2008»
13 years 11 months ago
Forecasting binary longitudinal data by a functional PC-ARIMA model
The purpose of this paper is to forecast the time evolution of a binary response variable from an associated continuous time series observed only at discrete time points that usual...
Ana M. Aguilera, Manuel Escabias, Mariano J. Valde...
MFCS
2005
Springer
14 years 4 months ago
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
We consider the problem of job scheduling on a variable voltage processor with d discrete voltage/speed levels. We give an algorithm which constructs a minimum energy schedule for ...
Minming Li, Frances F. Yao
AAAI
2006
14 years 8 days ago
Learning Basis Functions in Hybrid Domains
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Branislav Kveton, Milos Hauskrecht
UAI
1997
14 years 5 days ago
Nonuniform Dynamic Discretization in Hybrid Networks
We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable dis...
Alexander V. Kozlov, Daphne Koller