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» Learning for stochastic dynamic programming
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COLT
2008
Springer
15 years 4 months ago
Adapting to a Changing Environment: the Brownian Restless Bandits
In the multi-armed bandit (MAB) problem there are k distributions associated with the rewards of playing each of k strategies (slot machine arms). The reward distributions are ini...
Aleksandrs Slivkins, Eli Upfal
122
Voted
UML
2005
Springer
15 years 8 months ago
Lessons Learned from Developing a Dynamic OCL Constraint Enforcement Tool for Java
Analysis and design by contract allows the definition of a formal agreement between a class and its clients, expressing each party’s rights and obligations. Contracts written in ...
Wojciech J. Dzidek, Lionel C. Briand, Yvan Labiche
146
Voted
WSC
2000
15 years 3 months ago
Product-mix analysis with Discrete Event Simulation
Discrete Event Simulation (DES) has been used as a design and validation tool in various production and business applications. DES can also be utilized for analyzing the product-m...
Raid Al-Aomar
NIPS
1994
15 years 3 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore
ATMOS
2007
177views Optimization» more  ATMOS 2007»
15 years 4 months ago
Approximate dynamic programming for rail operations
Abstract. Approximate dynamic programming offers a new modeling and algorithmic strategy for complex problems such as rail operations. Problems in rail operations are often modeled...
Warren B. Powell, Belgacem Bouzaïene-Ayari