NDLI: On solving optimal policies for event-based dynamic programming

Content Provider	IEEE Xplore Digital Library
Author	Jia Qing-Shan
Copyright Year	2010
Description	Author affiliation: Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, Beijing 100084, China (Jia Qing-Shan)
Abstract	Markov decision processes (MDPs) have provided general frameworks for many control, decision making, and optimization problems. However, solving the optimal policies for many such problems is computationally prohibitive due to the large state space and the large action space. Event-based dynamic programming (EDP) has been developed to formulate the event-based decision making processes. Since the number of events could increase only linearly with respect to (w.r.t.) the problem scale, EDP provides a computationally feasible way to many problems which are time-consuming to solve in the MDP framework. However, the event sequence is not Markov, the optimal event-based policy could depend on the entire history, which cannot be implemented in practice. In this paper, for EDP with discrete and finite state space we construct a completely observable MDP with both the belief distribution over the internal system state and the current observable event being the state. Then we show that solving the original EDP is equivalent to solving this belief-event dynamic programming (BEDP), the optimal policies of which can be found within Markov policies that can be implemented in practice. Then potential-based policy iteration algorithms for completely observable MDP can be applied. We also discuss extensions to finite-stage EDP.
Starting Page	1511
Ending Page	1516
File Size	198038
Page Count	6
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Markov Decision Processes Decision making Discrete Event Dynamic Systems Process control Markov processes Aerospace electronics Event-based Dynamic Programming Dynamic programming History Approximation methods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Event-based optimization with lagged state information

An Iterative Procedure for Constructing Subsolutions of Discrete-Time Optimal Control Problems

A two-phase time aggregation algorithm for average cost Markov decision processes

Construction of embedded Markov decision processes for optimal control of non-linear systems with continuous state spaces

Parameterized penalties in the dual representation of Markov decision processes

An iterative scheme for the approximate linear programming solution to the optimal control of a Markov Decision Process

On Solving Optimal Policies for Finite-Stage Event-Based Optimization

A dynamic optimization decision and control model based on neighborhood systems

Study of Markov decision process-based optimal switching algorithm performance for small power systems

On solving optimal policies for event-based dynamic programming

Similar Documents

Event-based optimization with lagged state information

An Iterative Procedure for Constructing Subsolutions of Discrete-Time Optimal Control Problems

A two-phase time aggregation algorithm for average cost Markov decision processes

Construction of embedded Markov decision processes for optimal control of non-linear systems with continuous state spaces

Parameterized penalties in the dual representation of Markov decision processes

An iterative scheme for the approximate linear programming solution to the optimal control of a Markov Decision Process

On Solving Optimal Policies for Finite-Stage Event-Based Optimization

A dynamic optimization decision and control model based on neighborhood systems

Study of Markov decision process-based optimal switching algorithm performance for small power systems

On solving optimal policies for event-based dynamic programming