Stochastic Control of Partially Observable Systems PDF Download
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Author: Alain Bensoussan
Publisher: Cambridge University Press
ISBN: 052135403X
Category : Mathematics
Languages : en
Pages : 364
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Book Description
These systems play an important role in many applications.
Author: Alain Bensoussan
Publisher: Cambridge University Press
ISBN: 052135403X
Category : Mathematics
Languages : en
Pages : 364
Get Book Here
Book Description
These systems play an important role in many applications.
Author: Alain Bensoussan
Publisher:
ISBN:
Category :
Languages : en
Pages : 352
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Book Description
Author: Gaurav Marwah
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages :
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Book Description
A partially observable Markov decision process (POMDP) is a mathematical framework for planning and control problems in which actions have stochastic effects and observations provide uncertain state information. It is widely used for research in decision-theoretic planning and reinforcement learning. To cope with partial observability, a policy (or plan) must use memory, and previous work has shown that a finite-state controller provides a good policy representation. This thesis considers a previously-developed bounded policy iteration algorithm for POMDPs that finds policies that take the form of stochastic finite-state controllers. Two new improvements of this algorithm are developed. First improvement provides a simplification of the basic linear program, which is used to find improved controllers. This results in a considerable speed-up in efficiency of the original algorithm. Secondly, a branch and bound algorithm for adding the best possible node to the controller is presented, which provides an error bound and a test for global optimality. Experimental results show that these enhancements significantly improve the algorithm's performance.
Author: Yaakov Yavin
Publisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 248
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Book Description
Author: Alain Bensoussan
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Author: Wendell H. Fleming
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
This paper is concerned with the optimal control of continuous-time Markov processes. The admissible control laws are based on white-noise corrupted observations of a function on the state processes. A 'separated' control problem is introduced, whose states are probability measures on the original state space. The original and separated control problems are related via the nonlinear filter equation. The existence of a minimum for the separated problem is established. Under more restrictive assumptions it is shown that the minimum expected cost for the separated problem equals the infimum of expected costs for the original problem with partially observed states.
Author: Jesús Barreiro Hurlé
Publisher:
ISBN:
Category :
Languages : es
Pages : 20
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Author: Onésimo Hernández-Lerma
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Author: Yakoov Yavin
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
Author: Goong Chen
Publisher: CRC Press
ISBN: 9780849380754
Category : Business & Economics
Languages : en
Pages : 404
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Book Description
Linear Stochastic Control Systems presents a thorough description of the mathematical theory and fundamental principles of linear stochastic control systems. Both continuous-time and discrete-time systems are thoroughly covered. Reviews of the modern probability and random processes theories and the Itô stochastic differential equations are provided. Discrete-time stochastic systems theory, optimal estimation and Kalman filtering, and optimal stochastic control theory are studied in detail. A modern treatment of these same topics for continuous-time stochastic control systems is included. The text is written in an easy-to-understand style, and the reader needs only to have a background of elementary real analysis and linear deterministic systems theory to comprehend the subject matter. This graduate textbook is also suitable for self-study, professional training, and as a handy research reference. Linear Stochastic Control Systems is self-contained and provides a step-by-step development of the theory, with many illustrative examples, exercises, and engineering applications.
