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Near-Optimal BRL using Optimistic Local Transitions Mauricio Araya-L´ opez, Vincent Thomas, Olivier Buffet maraya|vthomas|[removed] LORIA, Campus scientifique, BP 239, 54506 Vandœuvre-ls-Nancy cedex, FRANCE
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Document Date: 2012-06-07 13:19:58


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City

R. Variance / Edinburgh / /

Company

BP / MIT Press / K. As R / /

Country

United Kingdom / Scotland / /

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Event

Man-Made Disaster / /

Facility

University of Massachusetts Amherst / /

IndustryTerm

baseline algorithm / deterministic heuristic algorithm / belief-lookahead algorithm / good solutions / polynomial time algorithm / heuristic algorithms / exploit-like algorithm / benchmark algorithms / analytic solution / approximate solutions / learning algorithms / /

Organization

MIT / University of Massachusetts Amherst / U.S. Securities and Exchange Commission / /

Person

Thomas / V / Vincent Thomas / /

Position

author / model at the current time step / representative / /

ProvinceOrState

Alaska / Rhode Island / /

PublishedMedium

Machine Learning / /

Technology

baseline algorithm / PAC-MDP algorithms / PAC Algorithms / Optimistic BRL Algorithms / polynomial time algorithm / deterministic heuristic algorithm / good RL algorithm / benchmark algorithms / RL algorithms / Machine Learning / BRL algorithms / FDM / Bayesian RL algorithm / belief-lookahead algorithm / classic RL algorithms / Typical RL algorithms / pdf / exploit-like algorithm / /

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