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» Learning for stochastic dynamic programming
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CG
2006
Springer
15 years 4 months ago
Feature Construction for Reinforcement Learning in Hearts
Temporal difference (TD) learning has been used to learn strong evaluation functions in a variety of two-player games. TD-gammon illustrated how the combination of game tree search...
Nathan R. Sturtevant, Adam M. White
UAI
2008
15 years 3 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
130
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GECCO
2010
Springer
249views Optimization» more  GECCO 2010»
15 years 4 months ago
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
Developing dispatching rules for manufacturing systems is a tedious process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and ob...
Torsten Hildebrandt, Jens Heger, Bernd Scholz-Reit...
IOR
2010
98views more  IOR 2010»
14 years 11 months ago
A Shadow Simplex Method for Infinite Linear Programs
We present a Simplex-type algorithm, that is, an algorithm that moves from one extreme point of the infinite-dimensional feasible region to another not necessarily adjacent extrem...
Archis Ghate, Dushyant Sharma, Robert L. Smith
MOBIHOC
2009
ACM
16 years 3 months ago
Energy-efficient capture of stochastic events by global- and local-periodic network coverage
We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset...
Shibo He, Jiming Chen, David K. Y. Yau, Huanyu Sha...