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IEEEPACT
2008
IEEE
16 years 19 days ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...
WEBI
2009
Springer
16 years 24 days ago
Adapting Reinforcement Learning for Trust: Effective Modeling in Dynamic Environments
—In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about...
Özgür Kafali, Pinar Yolum
CCGRID
2008
IEEE
16 years 21 days ago
Grid Differentiated Services: A Reinforcement Learning Approach
—Large scale production grids are a major case for autonomic computing. Following the classical definition of Kephart, an autonomic computing system should optimize its own beha...
Julien Perez, Cécile Germain-Renaud, Bal&aa...
CEC
2007
IEEE
16 years 17 days ago
Combine and compare evolutionary robotics and reinforcement Learning as methods of designing autonomous robots
—The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for...
Sergiu Goschin, Eduard Franti, Monica Dascalu, San...
CAMP
2005
IEEE
15 years 12 months ago
Reinforcement Learning for P2P Searching
— For a peer-to-peer (P2P) system holding massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured...
Luca Gatani, Giuseppe Lo Re, Alfonso Urso, Salvato...