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ICML
1996
IEEE
15 years 8 months ago
A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning
This paper presents a direct reinforcement learning algorithm, called Finite-Element Reinforcement Learning, in the continuous case, i.e. continuous state-space and time. The eval...
Rémi Munos
EOR
2006
66views more  EOR 2006»
15 years 3 months ago
Performance prediction of an unmanned airborne vehicle multi-agent system
Consider unmanned airborne vehicle (UAV) control agents in a dynamic multi-agent system. The agents must have a set of goals such as destination airport and intermediate positions...
Zhaotong Lian, Abhijit Deshmukh
ISCA
2009
IEEE
318views Hardware» more  ISCA 2009»
15 years 10 months ago
Thread criticality predictors for dynamic performance, power, and resource management in chip multiprocessors
With the shift towards chip multiprocessors (CMPs), exploiting and managing parallelism has become a central problem in computer systems. Many issues of parallelism management boi...
Abhishek Bhattacharjee, Margaret Martonosi
SAC
2010
ACM
15 years 10 months ago
MetaSelf: an architecture and a development method for dependable self-* systems
This paper proposes a software architecture and a development process for engineering dependable and controllable self-organising (SO) systems. Our approach addresses dependabilit...
Giovanna Di Marzo Serugendo, John S. Fitzgerald, A...
ICML
2007
IEEE
16 years 4 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...