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Error Bounds For Approximate Policy Iteration

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Please try the request again. 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. 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] Documents Authors Tables Log in Sign up MetaCart Donate Documents: Advanced Search Include Citations Authors: Advanced Search Include Citations | Disambiguate Tables: Error Bounds for Approximate Policy Iteration Cached Download have a peek here

The system returned: (22) Invalid argument The remote host or network may be down. They also depend on a new measure of the approximation power of the function space, the inherent Bellman residual, which reflects how well the function space is "aligned" with the dynamics The bounds are expressed in terms of the l\infty-norm of the average accumulated error as opposed to the l\infty-norm of the error in the case of the standard approximate value iteration 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 http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.1660

Error Bounds For Approximate Policy Iteration

Generated Mon, 10 Oct 2016 14:55:02 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 Please try the request again. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity. Keyphrases approximate policy iteration error bound dynamic programming discounted problem contraction property back-up operator value iteration policy iteration result Powered by: About CiteSeerX Submit and Index Documents Privacy Policy Help Data

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 See all ›18 CitationsSee all ›18 ReferencesShare Facebook Twitter Google+ LinkedIn Reddit Request full-text Error Bounds for Approximate Value Iteration.Conference Paper · January 2005 with 24 ReadsSource: DBLPConference: Proceedings, The Twentieth National Conference on Artificial 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.

Sequence of value rep- resentations Vn are processed iteratively by Vn+1 = AT Vn where T is the Bellman operator and A an approximation operator. 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. KappenRead full-textPoint-Based POMDP Algorithms: Improved Analysis and Implementation"This section presents a new convergence argument that draws on the two earlier approaches. The system returned: (22) Invalid argument The remote host or network may be down.

Please try the request again. The system returned: (22) Invalid argument The remote host or network may be down. Your cache administrator is webmaster. Although carefully collected, accuracy cannot be guaranteed.

Numerical experiments are used to substantiate the theoretical findings. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. Error Bounds For Approximate Policy Iteration We examine this theoretical results numerically by com- paring the performance of the approximate variants of DPP with existing reinforcement learning (RL) methods on different problem domains. The system returned: (22) Invalid argument The remote host or network may be down.

Your cache administrator is webmaster. http://megavoid.net/error-bounds/error-bounds-statistics.html The system returned: (22) Invalid argument The remote host or network may be down. The system returned: (22) Invalid argument The remote host or network may be down. 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

Your cache administrator is webmaster. An important feature of our proof technique is that it permits the study of weighted Lp-norm performance bounds. Generated Mon, 10 Oct 2016 14:55:02 GMT by s_ac15 (squid/3.5.20) Check This Out Your cache administrator is webmaster.

Our main results come in the form of finite-time bounds on the performance of two versions of sampling-based FVI. Generated Mon, 10 Oct 2016 14:55:02 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://0.0.0.7/ Connection The system returned: (22) Invalid argument The remote host or network may be down.

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

rgreq-4041ee9bba3f56d1750a756cb43a7d4f false ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.3/ Connection to 0.0.0.3 failed. The system returned: (22) Invalid argument The remote host or network may be down. All rights reserved.About us · Contact us · Careers · Developers · News · Help Center · Privacy · Terms · Copyright | Advertising · Recruiting We use cookies to give you the best possible experience on ResearchGate. The conditions of the main result, as well as the concepts introduced in the analysis, are extensively discussed and compared to previous theoretical results.

Please try the request again. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Its use of weighted max-norm machinery in value iteration is closely related to [Munos, 2004]. http://megavoid.net/error-bounds/error-bounds.html Generated Mon, 10 Oct 2016 14:55:02 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://0.0.0.10/ Connection

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 Generated Mon, 10 Oct 2016 14:55:02 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 Generated Mon, 10 Oct 2016 14:55:02 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://0.0.0.5/ Connection Please try the request again.

Your cache administrator is webmaster. Your cache administrator is webmaster. Here are the instructions how to enable JavaScript in your web browser. For finite state-space MDPs, working with a finite set of representative states Munos (2003 Munos ( , 2005) considered planning scenarios with known dynamics analyzing the stability of both approximate policy

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. 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. Your cache administrator is webmaster. Generated Mon, 10 Oct 2016 14:55:02 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://0.0.0.4/ Connection

Our results show that, in all cases, DPP-based algorithms outperform other RL methods by a wide margin. Your cache administrator is webmaster. Full-text · Article · Jun 2008 Rémi MunosCsaba SzepesváriRead full-textShow moreRecommended publicationsArticleAnalyse en norme Lp de l'algorithme d'itérations sur les valeurs avec approximationsOctober 2016 · Revue d intelligence artificielleRemi MunosRead moreArticlePerformance Publisher conditions are provided by RoMEO.

The results extend usual analysis in L1-norm, and allow to relate the performance of AVI to the approximation power (usually expressed in Lp-norm, for p = 1 or 2) of the Full-text · Article · Nov 2012 Mohammad Gheshlaghi AzarVicenc GomezHilbert J. Generated Mon, 10 Oct 2016 14:55:02 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 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

We prove the finite-iteration and asymptotic l\infty-norm performance-loss bounds for DPP in the presence of approximation/estimation error. Please try the request again. Please try the request again.