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GECCO
2004
Springer
147views Optimization» more  GECCO 2004»
13 years 10 months ago
A Demonstration of Neural Programming Applied to Non-Markovian Problems
Genetic programming may be seen as a recent incarnation of a long-held goal in evolutionary computation: to develop actual computational devices through evolutionary search. Geneti...
Gabriel Catalin Balan, Sean Luke
JAIR
2006
157views more  JAIR 2006»
13 years 4 months ago
Decision-Theoretic Planning with non-Markovian Rewards
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic...
Sylvie Thiébaux, Charles Gretton, John K. S...
JCP
2007
143views more  JCP 2007»
13 years 4 months ago
Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
Abstract— We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths algorithm. We consider an a...
Nicolas Chapados, Yoshua Bengio
CORR
2011
Springer
219views Education» more  CORR 2011»
12 years 12 months ago
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing informa...
Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla
ESANN
2004
13 years 6 months ago
High-accuracy value-function approximation with neural networks applied to the acrobot
Several reinforcement-learning techniques have already been applied to the Acrobot control problem, using linear function approximators to estimate the value function. In this pape...
Rémi Coulom