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» Variational methods for Reinforcement Learning
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GECCO
2006
Springer
177views Optimization» more  GECCO 2006»
13 years 11 months ago
Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...
Martin V. Butz, Pier Luca Lanzi, Stewart W. Wilson
ICRA
2007
IEEE
155views Robotics» more  ICRA 2007»
14 years 2 months ago
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control
— The least squares approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular an...
Masashi Sugiyama, Hirotaka Hachiya, Christopher To...
ATAL
2004
Springer
14 years 1 months ago
Adaptive, Distributed Control of Constrained Multi-Agent Systems
Product Distribution (PD) theory was recently developed as a framework for analyzing and optimizing distributed systems. In this paper we demonstrate its use for adaptive distribu...
Stefan Bieniawski, David Wolpert
NIPS
2007
13 years 9 months ago
Receding Horizon Differential Dynamic Programming
The control of high-dimensional, continuous, non-linear dynamical systems is a key problem in reinforcement learning and control. Local, trajectory-based methods, using techniques...
Yuval Tassa, Tom Erez, William D. Smart
ACL
2010
13 years 5 months ago
Optimising Information Presentation for Spoken Dialogue Systems
We present a novel approach to Information Presentation (IP) in Spoken Dialogue Systems (SDS) using a data-driven statistical optimisation framework for content planning and attri...
Verena Rieser, Oliver Lemon, Xingkun Liu