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» Iterative Learning Control - Monotonicity and Optimization
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NIPS
1993
14 years 11 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
GECCO
2008
Springer
123views Optimization» more  GECCO 2008»
14 years 11 months ago
Hierarchical evolution of linear regressors
We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learn...
Francesc Teixidó-Navarro, Albert Orriols-Pu...
ICRA
2008
IEEE
156views Robotics» more  ICRA 2008»
15 years 4 months ago
Consensus learning for distributed coverage control
— A decentralized controller is presented that causes a network of robots to converge to a near optimal sensing configuration, while simultaneously learning the distribution of ...
Mac Schwager, Jean-Jacques E. Slotine, Daniela Rus
GECCO
2000
Springer
142views Optimization» more  GECCO 2000»
15 years 1 months ago
Controlling Effective Introns for Multi-Agent Learning by Genetic Programming
This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the effective cooperative behav...
Hitoshi Iba, Makoto Terao
ICNC
2005
Springer
15 years 3 months ago
A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps
Abstract. Self-Organizing Maps (SOM) is a powerful tool for clustering and discovering patterns in data. Competitive learning in the SOM training process focusses on finding a neu...
Joseph P. Herbert, Jingtao Yao