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Error Bounds For Approximate Value Iteration

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Why Does this Site Require Cookies? Please try the request again. We illustrate the tightness of these bounds on an optimal replacement problem.Do you want to read the rest of this conference paper?Request full-text CitationsCitations18ReferencesReferences18Dynamic Policy Programming"where v πn denotes the value You need to reset your browser to accept cookies or to ask you if you want to accept cookies. have a peek here

Preliminary versions of the results presented here were published in (Szepesvári and Munos, 2005). "[Show abstract] [Hide abstract] ABSTRACT: In this paper we develop a theoretical analysis of the performance of Your cache administrator is webmaster. The system returned: (22) Invalid argument The remote host or network may be down. Generated Mon, 10 Oct 2016 14:42:22 GMT by s_ac15 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.6/ Connection see here

Error Bounds For Approximate Value Iteration

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Please try the request again. Bounds on the error between the performance of the policies induced by the algorithm and the optimal policy are given as a function of weighted L_p-norms (p>=1) of the approximation errors. Your browser does not support cookies. Please try the request again.

Sequence of value representations Vn are processed iteratively by Vn+1 = A T Vn where T is the Bellman operator and A an approximation operator. The conditions of the main result, as well as the concepts introduced in the analysis, are extensively discussed and compared to previous theoretical results. Although carefully collected, accuracy cannot be guaranteed. navigate to this website KappenRead full-textPoint-Based POMDP Algorithms: Improved Analysis and Implementation"This section presents a new convergence argument that draws on the two earlier approaches.

Full-text · Article · Jul 2012 · Journal of Machine Learning ResearchTrey SmithReid SimmonsRead full-textFinite-Time Bounds for Fitted Value Iteration"The work presented here builds on and extends our previous work. This suggests that DPP can achieve a better performance than AVI and API since it averages out the simulation noise caused by Monte-Carlo sampling throughout the learning process. Our argument reflects current point-based algorithms in that it allows B to be a non-uniform sampling of ¯ ∆ whose spacing varies according to discounted reachability . "[Show abstract] [Hide abstract] We prove the finite-iteration and asymptotic l\infty-norm performance-loss bounds for DPP in the presence of approximation/estimation error.

To accept cookies from this site, use the Back button and accept the cookie. Its use of weighted max-norm machinery in value iteration is closely related to [Munos, 2004]. Error Bounds For Approximate Value Iteration The system returned: (22) Invalid argument The remote host or network may be down. All rights reserved.

Numerical experiments are used to substantiate the theoretical findings. http://megavoid.net/error-bounds/error-bounds-statistics.html This site stores nothing other than an automatically generated session ID in the cookie; no other information is captured. Here are the instructions how to enable JavaScript in your web browser. For the mountain-car problem, we use the root mean-squared error (RMSE) between the value function under the policy induced by the algorithm at iteration n and the optimal value functions, since

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As a result, our technique applies to a large class of function-approximation methods (e.g., neural networks, adaptive regression trees, kernel machines, locally weighted learning), and our bounds scale well with the The bounds show a dependence on the stochastic stability properties of the MDP: they scale with the discounted-average concentrability of the future-state distributions. Error Bounds for Approximate Value Iteration Rémi Munos Approximate Value Iteration (AVI) is an method for solving a Markov Decision Problem by making successive calls to a supervised learning (SL) algorithm.

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Setting Your Browser to Accept Cookies There are many reasons why a cookie could not be set correctly. Full-text · Article · Nov 2012 Mohammad Gheshlaghi AzarVicenc GomezHilbert J. The convergence rate results obtained allow us to show that both versions of FVI are well behaving in the sense that by using a sufficiently large number of samples for a Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity.

The results extend usual analysis in L_infinity-norm, and allow to relate the performance of AVI to the approximation power (usually expressed in L_p-norm, for p=1 or 2) of the SL algorithm. Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.Learn moreLast Updated: 14 Sep 16 © 2008-2016 researchgate.net. Generated Mon, 10 Oct 2016 14:42:22 GMT by s_ac15 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.8/ Connection http://megavoid.net/error-bounds/error-bounds.html Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.

Generated Mon, 10 Oct 2016 14:42:22 GMT by s_ac15 (squid/3.5.20) ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.9/ Connection Read our cookies policy to learn more.OkorDiscover by subject areaRecruit researchersJoin for freeLog in EmailPasswordForgot password?Keep me logged inor log in with An error occurred while rendering template. Bounds on the error between the performance of the policies induced by the algorithm and the optimal policy are given as a function of weighted Lp-norms (p 1) of the approximation