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Matrix theory / Functional analysis / Spectral theory / Automatic basis function construction / Dynamic programming / Proto-value functions / Static single assignment form / Reinforcement learning / Singular value decomposition / Algebra / Mathematics / Linear algebra


An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning Ronald Parr? Lihong Li† Gavin Taylor? Christopher Painter-Wakefield?
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Document Date: 2008-05-13 13:04:46

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City

Piscataway / Helsinki / /

Company

Uncontrolled Case / MIT Press / MEBF S A / Markov / /

Country

Jordan / United States / Finland / /

Facility

Duke University / Rutgers University / Purdue University / /

IndustryTerm

fixed-point solution / linear fixedpoint solution / neural networks / least-squares solution / policy-improvement algorithms / dot product / linear fixed-point solution / Linear least-squares algorithms / linear-model solution / iterative solution / model-free and model-based algorithms / feature-selection algorithms / linear model solution / /

Organization

National Science Foundation / Department of Computer Science / MIT / Rutgers University / Purdue University / Duke University / Durham / /

Person

Michael L. Littman / Gavin Taylor / Jeff Johns / Sridhar Mahadevan / Xiaobai Sun / Ronald Parr / Carlo Tomasi / Marek Petrik / Christopher Painter-Wakefield / /

Position

author / PVF-MP / Reinforcement Learning PVF−MP / Reward Error PVF PVF−MP / MP / Eig-MP and PVF-MP / Model minimization / Bellman error BEBF Eig−MP / Eig-MP / Dean / /

Product

Pentax K-x Digital Camera / /

ProgrammingLanguage

E / V / L / R / /

ProvinceOrState

New Jersey / North Carolina / /

PublishedMedium

Machine Learning / /

Technology

Linear least-squares algorithms / LSPI algorithm / remaining algorithms / model-free and model-based algorithms / BEBF algorithm / WL algorithm / existing feature-selection algorithms / Machine Learning / known feature-selection algorithms / policy-improvement algorithms / LSTD algorithm / four algorithms / 4 algorithms / /

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