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Error Bounds For Approximate Policy Iteration Bibtex

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Handbook of Intelligent Control. UAI 2007: 67-74[i1]viewelectronic edition @ arxiv.orgexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:journals/corr/abs-cs-0703062ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/journals/corr/abs-cs-0703062Pierre-Arnaud Coquelin, Rémi Munos: Bandit Algorithms for Tree Search. Moore: Rates of Convergence for Variable Resolution Schemes in Optimal Control. Approximate Simulation-Based Solution of Large-scale Least Squares Problems. have a peek here

Perspectives in Mathematical Science — I: Probability and Statistics, World Scientific Publishing Co., 2009: 71–91.[15]F. K. J. Machine Learning, 1988, 3(1): 9–44.[29]G. http://dl.acm.org/citation.cfm?id=964290

Error Bounds For Approximate Policy Iteration Bibtex

IBM Journal of Research and Development, 1967, 11(6): 601–617.CrossRef[27]A. On the last classification-based implementation, we develop a finite-sample analysis that shows that MPI's main parameter allows to control the balance between the estimation error of the classifier and the overall Basis function adaptation in temporal difference reinforcement learning. Machine Learning 49(2-3): 291-323 (2002)2001[c9]viewelectronic edition @ nips.ccelectronic edition @ cmu.eduexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:conf/nips/Munos01ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/conf/nips/Munos01Rémi Munos: Efficient Resources Allocation for Markov Decision Processes.

P. Feature-Based Methods for Large Scale Dynamic Programming. V. Kroese.

of ?? NIPS 2007: 9-16[c16]viewelectronic edition @ dslpitt.orgexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:conf/uai/CoquelinM07ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/conf/uai/CoquelinM07Pierre-Arnaud Coquelin, Rémi Munos: Bandit Algorithms for Tree Search. Optimal stopping of Markov processes: hilbert space theory, approximation algorithms, and an application to pricing financial derivatives. De Schutter, et al.

Sci. 558: 77-106 (2014)[c78]viewelectronic edition @ aaai.orgexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:conf/aaai/KocakVMA14ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/conf/aaai/KocakVMA14Tomás Kocák, Michal Valko, Rémi Munos, Shipra Agrawal: Spectral Thompson Sampling. doi:10.1007/s11768-011-1005-3 41 Citations 435 Views AbstractWe consider the classical policy iteration method of dynamic programming (DP), where approximations and simulation are used to deal with the curse of dimensionality. A tutorial on the cross-entropy method. Improvements on learning tetris with cross-entropy.

P. his comment is here Generated Mon, 10 Oct 2016 14:49:10 GMT by s_ac15 (squid/3.5.20) Error Bounds For Approximate Policy Iteration Bibtex Discrete Event Dynamic Systems: Theory and Applications, 2006, 16(2): 207–239.MathSciNetMATHCrossRef[64]J. Cambridge: MIT Press, 1998.[3]A.

ICML 2012[c53]viewelectronic edition @ nips.ccexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:conf/nips/CarpentierM12ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/conf/nips/CarpentierM12Alexandra Carpentier, Rémi Munos: Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. http://megavoid.net/error-bounds/error-bounds-statistics.html Real-time learning and control using asynchronous dynamic programming. Report LIDS-P-2822. New York: Springer-Verlag, 2001.MATH[65]R.

degree in Electrical Engineering at the George Washington University, Washington D.C. Our discussion of policy evaluation is couched in general terms and aims to unify the available methods in the light of recent research developments and to compare the two main policy Wang, D. Check This Out Solution of Large Systems of Equations Using Approximate Dynamic Programming Methods.

Stochastic Approximation: A Dynamical Systems Viewpoint. A. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010: 1207–1214.[43]S.

Daniels[c65] 18Ivo Danihelka[i22] 19Romain Deguest[c25] [c20] 20Amir Massoud Farahmand[c33] 21Ilias Flaounas[c58] 22Ilias N.

T. Differential training of rollout policies. Most current methods are geared towards exploiting the regularities of either the value function or the policy. A natural policy gradient.

NIPS 2008: 1729-1736[e1]viewtable of contents in dblpexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:conf/ewrl/2008ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/conf/ewrl/2008Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko: Recent Advances in Reinforcement Learning, F. Report 2731. http://megavoid.net/error-bounds/error-bounds.html A.

Van Roy. Sci. 558: 62-76 (2014)[j17]viewelectronic edition via DOIexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:journals/tcs/CarpentierM14ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/journals/tcs/CarpentierM14Alexandra Carpentier, Rémi Munos: Minimax number of strata for online stratified sampling: The case of More information Accept Over 10 million scientific documents at your fingertips Switch Edition Academic Edition Corporate Edition Home Impressum Legal Information Contact Us © 2016 Springer International Publishing. A., Rémi Munos: Online gradient descent for least squares regression: Non-asymptotic bounds and application to bandits.

Tsitsiklis, B. Bertsekas, E. The system returned: (22) Invalid argument The remote host or network may be down. SIAM Review, 1967, 9(2): 165–177.MathSciNetMATHCrossRef[95]D.

Bertsekas. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos: Unifying Count-Based Exploration and Intrinsic Motivation. TD is related to dynamic programming techniques because it approximates its current estimate based on previously learned estimates (a process known as bootstrapping). CoRR abs/1205.4217 (2012)[i4]viewelectronic edition @ arxiv.orgexport recordBibTeXRISRDF N-TriplesRDF/XMLXMLdblp key:journals/corr/abs-1209-2693ask othersGoogleGoogle ScholarMS Academic SearchCiteSeerXSemantic Scholarshare recordTwitterRedditMendeleyBibSonomyLinkedInGoogle+Facebookshort URL:http://dblp.org/rec/journals/corr/abs-1209-2693Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos: Regret Bounds for Restless Markov Bandits.

Kroese, S. Most real-world MDPs are too large for such a representation to be feasible, preventing the use of exact MDP algorithms. P. We also illustrate this approach empirically on several problems, including a large HIV control task.

Basis function adaptation methods for cost approximation in MDP. The system returned: (22) Invalid argument The remote host or network may be down. Bertsekas. L.