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» On an anytime algorithm for control
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NIPS
2008
15 years 7 months ago
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
Jo-Anne Ting, Mrinal Kalakrishnan, Sethu Vijayakum...
CORR
2010
Springer
157views Education» more  CORR 2010»
15 years 6 months ago
Joint Bandwidth and Power Allocation with Admission Control in Wireless Multi-User Networks With and Without Relaying
Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Joint bandwidth and power allocation s...
Xiaowen Gong, Sergiy A. Vorobyov, Chintha Tellambu...
IJRR
2008
186views more  IJRR 2008»
15 years 6 months ago
Automated Design of Adaptive Controllers for Modular Robots using Reinforcement Learning
Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used bo...
Paulina Varshavskaya, Leslie Pack Kaelbling, Danie...
IJON
2006
90views more  IJON 2006»
15 years 6 months ago
Reinforcement learning of a simple control task using the spike response model
In this work, we propose a variation of a direct reinforcement learning algorithm, suitable for usage with spiking neurons based on the spike response model (SRM). The SRM is a bi...
Murilo Saraiva de Queiroz, Roberto Coelho de Berr&...
IJRR
2006
76views more  IJRR 2006»
15 years 6 months ago
From Robots to Animals: Virtual Fences for Controlling Cattle
We consider the problem of monitoring and controlling the position of herd animals, and view animals as agents with natural mobility but not strictly controllable. By exploiting k...
Zack J. Butler, Peter I. Corke, Ronald A. Peterson...