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Approximate Policy Iteration Algorit...

Ma, Jun.

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Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis./
Author:	Ma, Jun.
Description:	161 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
Contained By:	Dissertation Abstracts International72-06B.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3452619
ISBN:	9781124595061

Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis.
Ma, Jun.

Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis. - 161 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .

Thesis (Ph.D.)--Princeton University, 2011.

The purpose of this dissertation is to present parametric and non-parametric policy iteration algorithms that handle Markov decision process problems with high-dimensional, continuous state and action spaces and to conduct convergence analysis of these algorithms under a variety of technical conditions. An online, on-policy least-squares policy iteration (LSPI) algorithm is proposed, which can be applied to infinite horizon problems with where states and controls are vector-valued and continuous. No special problem structure such as linear, additive noise is assumed, and the expectation is assumably uncomputable. The concept of the post-decision state variable is used to eliminate the expectation inside the optimization problem, and a formal convergence analysis of the algorithm is provided under the assumption that value functions are spanned by finitely many known basis functions. Furthermore, the convergence result extends to the more general case of unknown value function form using orthogonal polynomial approximation. Using kernel smoothing techniques, this dissertation presents three different online, on-policy approximate policy iteration algorithms which can be applied to infinite horizon problems with continuous and high-dimensional state and action spaces. They are kernel-based least squares approximate policy iteration, approximate policy iteration with kernel smoothing and policy iteration with finite horizon approximation and kernel estimators. The use of Monte Carlo sampling to estimate the value function around the post-decision state reduces the problem to a sequence of deterministic, nonlinear programming problems that allow the algorithms to handle continuous, vector-valued states and actions. Again, a formal convergence analysis of the algorithms under a variety of technical assumptions is presented. The algorithms are applied to different numerical applications including linear quadratic regulation, wind energy allocation and battery storage problems to demonstrate their effectiveness and convergence properties.

ISBN: 9781124595061Subjects--Topical Terms:

626628
Business Administration, Management.

Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis.
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245 1 0 $a Approximate Policy Iteration Algorithms for Continuous, Multidimensional Applications and Convergence Analysis.
300 $a 161 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
500 $a Adviser: Warren B. Powell.
502 $a Thesis (Ph.D.)--Princeton University, 2011.
520 $a The purpose of this dissertation is to present parametric and non-parametric policy iteration algorithms that handle Markov decision process problems with high-dimensional, continuous state and action spaces and to conduct convergence analysis of these algorithms under a variety of technical conditions. An online, on-policy least-squares policy iteration (LSPI) algorithm is proposed, which can be applied to infinite horizon problems with where states and controls are vector-valued and continuous. No special problem structure such as linear, additive noise is assumed, and the expectation is assumably uncomputable. The concept of the post-decision state variable is used to eliminate the expectation inside the optimization problem, and a formal convergence analysis of the algorithm is provided under the assumption that value functions are spanned by finitely many known basis functions. Furthermore, the convergence result extends to the more general case of unknown value function form using orthogonal polynomial approximation. Using kernel smoothing techniques, this dissertation presents three different online, on-policy approximate policy iteration algorithms which can be applied to infinite horizon problems with continuous and high-dimensional state and action spaces. They are kernel-based least squares approximate policy iteration, approximate policy iteration with kernel smoothing and policy iteration with finite horizon approximation and kernel estimators. The use of Monte Carlo sampling to estimate the value function around the post-decision state reduces the problem to a sequence of deterministic, nonlinear programming problems that allow the algorithms to handle continuous, vector-valued states and actions. Again, a formal convergence analysis of the algorithms under a variety of technical assumptions is presented. The algorithms are applied to different numerical applications including linear quadratic regulation, wind energy allocation and battery storage problems to demonstrate their effectiveness and convergence properties.
590 $a School code: 0181.
650 4 $a Business Administration, Management. $3 626628
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Operations Research. $3 626629
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710 2 $a Princeton University. $3 645579
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Powell, Warren B., $e advisor
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791 $a Ph.D.
792 $a 2011
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