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FLAIRS
1998
13 years 11 months ago
Optimizing Production Manufacturing Using Reinforcement Learning
Manyindustrial processes involve makingparts with an assemblyof machines, where each machinecarries out an operation on a part, and the finished product requires a wholeseries of ...
Sridhar Mahadevan, Georgios Theocharous
AAAI
1996
13 years 11 months ago
Learning to Take Actions
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...
Roni Khardon
ICML
2000
IEEE
14 years 11 months ago
Reinforcement Learning in POMDP's via Direct Gradient Ascent
This paper discusses theoretical and experimental aspects of gradient-based approaches to the direct optimization of policy performance in controlled ??? ?s. We introduce ??? ?, a...
Jonathan Baxter, Peter L. Bartlett
EURONGI
2005
Springer
14 years 3 months ago
An Afterstates Reinforcement Learning Approach to Optimize Admission Control in Mobile Cellular Networks
We deploy a novel Reinforcement Learning optimization technique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction inform...
José Manuel Giménez-Guzmán, J...
COOPIS
2003
IEEE
14 years 3 months ago
Learning to Invoke Web Forms
Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of information agents a...
Nicholas Kushmerick