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» Reinforcement Learning with Hierarchies of Machines
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ALT
1994
Springer
15 years 1 months ago
Program Synthesis in the Presence of Infinite Number of Inaccuracies
Most studies modeling inaccurate data in Gold style learning consider cases in which the number of inaccuracies is finite. The present paper argues that this approach is not reaso...
Sanjay Jain
ICML
2009
IEEE
15 years 10 months ago
Regularization and feature selection in least-squares temporal difference learning
We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (L...
J. Zico Kolter, Andrew Y. Ng
ICMLA
2010
14 years 7 months ago
Robust Learning for Adaptive Programs by Leveraging Program Structure
Abstract--We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic...
Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig
ML
2000
ACM
150views Machine Learning» more  ML 2000»
14 years 9 months ago
Adaptive Retrieval Agents: Internalizing Local Context and Scaling up to the Web
This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information ...
Filippo Menczer, Richard K. Belew
66
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COLT
1989
Springer
15 years 1 months ago
Learning in the Presence of Inaccurate Information
The present paper considers the effects of introducing inaccuracies in a learner’s environment in Gold’s learning model of identification in the limit. Three kinds of inaccu...
Mark A. Fulk, Sanjay Jain