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Goal-Directed Online Learning of Predictive Models
- Source :
- Lecture Notes in Computer Science ISBN: 9783642299452, EWRL
- Publication Year :
- 2012
- Publisher :
- Springer Berlin Heidelberg, 2012.
-
Abstract
- We present an algorithmic approach for integrated learning and planning in predictive representations. The approach extends earlier work on predictive state representations to the case of online exploration, by allowing exploration of the domain to proceed in a goal-directed fashion and thus be more efficient. Our algorithm interleaves online learning of the models, with estimation of the value function. The framework is applicable to a variety of important learning problems, including scenarios such as apprenticeship learning, model customization, and decision-making in non-stationary domains.
- Subjects :
- Computer science
Active learning (machine learning)
business.industry
Algorithmic learning theory
Online machine learning
Semi-supervised learning
Machine learning
computer.software_genre
Robot learning
Apprenticeship learning
Unsupervised learning
Instance-based learning
Artificial intelligence
business
computer
Subjects
Details
- ISBN :
- 978-3-642-29945-2
- ISBNs :
- 9783642299452
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783642299452, EWRL
- Accession number :
- edsair.doi...........680be953afab0de0d853ab9a7291b0f6