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» Optimizing interconnection policies
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ATAL
2010
Springer
15 years 5 months ago
Frequency adjusted multi-agent Q-learning
Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
Michael Kaisers, Karl Tuyls
INFORMATICALT
2010
119views more  INFORMATICALT 2010»
15 years 2 months ago
Tight Arrival Curve at the Output of a Work-Conserving Blind Multiplexing Server
Abstract. As a means of supporting quality of service guarantees, aggregate multiplexing has attracted a lot of attention in the networking community, since it requires less comple...
Juan Echagüe, Vicent Cholvi
IOR
2008
89views more  IOR 2008»
15 years 4 months ago
Impulse Control of Brownian Motion: The Constrained Average Cost Case
When a manufacturer places repeated orders with a supplier to meet changing production requirements, he faces the challenge of finding the right balance between holding costs and ...
Melda Ormeci, J. G. Dai, John H. Vande Vate
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
15 years 9 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn
ECRTS
1998
IEEE
15 years 8 months ago
Using exact feasibility tests for allocating real-time tasks in multiprocessor systems
This paper introduces improvements in partitioning schemes for multiprocessor real-time systems which allow higher processor utilization and enhanced schedulability by using exact...
Sergio Saez, Joan Vila i Carbó, Alfons Cres...