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Estimation theory / Machine learning / Conditional random field / Theoretical computer science / Reinforcement learning / Loss function / Markov decision process / Rao–Blackwell theorem / Bayesian network / Statistics / Graphical models / Statistical theory


Conditional Random Fields for Multi-agent Reinforcement Learning Xinhua Zhang [removed] Douglas Aberdeen [removed]
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Document Date: 2009-06-21 14:46:49

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City

Arlington / /

Company

AUAI Press / CRF / Monte Carlo / Bernstein / Baxter / /

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Facility

Australian National University / /

IndustryTerm

statistical gradient-following algorithms / car waiting / distributed sensor network / policy-gradient algorithms / temporal learning algorithms / sensor networks / multiagent systems / energy level / road traffic network / inner product / online settings / policy search / simplest algorithms / travel time / policy-gradient solution / online variation / Online Natural / approximate sampling algorithms / search space / use batch training algorithms / sensor network / /

MarketIndex

SET 50 / /

Organization

Centre of Excellence / Cambridge Univ. / Australian Government / Australian National University / Canberra / NICTA Research School of Information Sciences & Engineering / /

Person

Douglas Aberdeen / Max Min / Mansour / /

Position

author / model the optimal joint policy / Online Natural Actor-Critic / Natural actor-critic / β=actor / log proba2 We use rt / model the proper joint policy / rT / =critic / critic / model control processes / controller / /

Product

CRFs / /

ProvinceOrState

Virginia / /

PublishedMedium

Machine Learning / /

Technology

approximate sampling algorithms / NAC algorithm / CRFs use batch training algorithms / simplest algorithms / RL algorithms / temporal learning algorithms / Machine Learning / statistical gradient-following algorithms / policy-gradient algorithms / /

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