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» Qualitative reinforcement learning
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SMC
2007
IEEE
118views Control Systems» more  SMC 2007»
14 years 3 months ago
One-class learning with multi-objective genetic programming
One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multiobjective genetic programming (GP) all...
Robert Curry, Malcolm I. Heywood
FLAIRS
2008
13 years 11 months ago
Learning Continuous Action Models in a Real-Time Strategy Environment
Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typic...
Matthew Molineaux, David W. Aha, Philip Moore
GECCO
2005
Springer
153views Optimization» more  GECCO 2005»
14 years 2 months ago
Evolving neural network ensembles for control problems
In neuroevolution, a genetic algorithm is used to evolve a neural network to perform a particular task. The standard approach is to evolve a population over a number of generation...
David Pardoe, Michael S. Ryoo, Risto Miikkulainen
IBERAMIA
2010
Springer
13 years 7 months ago
Dynamic Reward Shaping: Training a Robot by Voice
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to ...
Ana C. Tenorio-Gonzalez, Eduardo F. Morales, Luis ...
ICML
2008
IEEE
14 years 10 months ago
Automatic discovery and transfer of MAXQ hierarchies
We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian network models to a successful...
Neville Mehta, Soumya Ray, Prasad Tadepalli, Thoma...