Sciweavers

8 search results - page 1 / 2
» Global Versus Local Constructive Function Approximation for ...
Sort
View
AUSAI
2005
Springer
13 years 10 months ago
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Peter Vamplew, Robert Ollington
EUROGP
2009
Springer
130views Optimization» more  EUROGP 2009»
13 years 11 months ago
One-Class Genetic Programming
One-class classification naturally only provides one-class of exemplars, the target class, from which to construct the classification model. The one-class approach is constructed...
Robert Curry, Malcolm I. Heywood
JMLR
2006
153views more  JMLR 2006»
13 years 4 months ago
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of c...
Jelle R. Kok, Nikos A. Vlassis
NIPS
1993
13 years 6 months ago
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming
Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have ...
Christopher G. Atkeson
AAAI
1998
13 years 6 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore