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ML
2002
ACM
114views Machine Learning» more  ML 2002»
13 years 9 months ago
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
Amy McGovern, J. Eliot B. Moss, Andrew G. Barto
IJON
2006
90views more  IJON 2006»
13 years 10 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&...
CIG
2006
IEEE
14 years 4 months ago
Monte-Carlo Go Reinforcement Learning Experiments
Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture...
Bruno Bouzy, Guillaume Chaslot
ECAI
2008
Springer
13 years 11 months ago
Reinforcement Learning with the Use of Costly Features
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Robby Goetschalckx, Scott Sanner, Kurt Driessens
NIPS
1996
13 years 11 months ago
Reinforcement Learning for Mixed Open-loop and Closed-loop Control
Closed-loop control relies on sensory feedback that is usually assumed to be free. But if sensing incurs a cost, it may be coste ective to take sequences of actions in open-loop m...
Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstei...