Relevant bibliographies by topics / Approximate dynamic programming

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Approximate dynamic programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Approximate dynamic programming"

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Powell, Warren B. "Perspectives of approximate dynamic programming." Annals of Operations Research 241, no. 1-2 (2012): 319–56. http://dx.doi.org/10.1007/s10479-012-1077-6.

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Kulkarni, Sameer, Rajesh Ganesan, and Lance Sherry. "Dynamic Airspace Configuration Using Approximate Dynamic Programming." Transportation Research Record: Journal of the Transportation Research Board 2266, no. 1 (2012): 31–37. http://dx.doi.org/10.3141/2266-04.

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On the basis of weather and high traffic, the Next Generation Air Transportation System envisions an airspace that is adaptable, flexible, controller friendly, and dynamic. Sector geometries, developed with average traffic patterns, have remained structurally static with occasional changes in geometry due to limited forming of sectors. Dynamic airspace configuration aims at migrating from a rigid to a more flexible airspace structure. Efficient management of airspace capacity is important to ensure safe and systematic operation of the U.S. National Airspace System and maximum benefit to stakeh
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de Farias, D. P., and B. Van Roy. "The Linear Programming Approach to Approximate Dynamic Programming." Operations Research 51, no. 6 (2003): 850–65. http://dx.doi.org/10.1287/opre.51.6.850.24925.

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Logé, Frédéric, Erwan Le Pennec, and Habiboulaye Amadou-Boubacar. "Intelligent Questionnaires Using Approximate Dynamic Programming." i-com 19, no. 3 (2020): 227–37. http://dx.doi.org/10.1515/icom-2020-0022.

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Abstract Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episo
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Ryzhov, Ilya O., Martijn R. K. Mes, Warren B. Powell, and Gerald van den Berg. "Bayesian Exploration for Approximate Dynamic Programming." Operations Research 67, no. 1 (2019): 198–214. http://dx.doi.org/10.1287/opre.2018.1772.

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Maxwell, Matthew S., Mateo Restrepo, Shane G. Henderson, and Huseyin Topaloglu. "Approximate Dynamic Programming for Ambulance Redeployment." INFORMS Journal on Computing 22, no. 2 (2010): 266–81. http://dx.doi.org/10.1287/ijoc.1090.0345.

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Coşgun, Özlem, Ufuk Kula, and Cengiz Kahraman. "Markdown Optimization via Approximate Dynamic Programming." International Journal of Computational Intelligence Systems 6, no. 1 (2013): 64–78. http://dx.doi.org/10.1080/18756891.2013.754181.

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El-Rayes, Khaled, and Hisham Said. "Dynamic Site Layout Planning Using Approximate Dynamic Programming." Journal of Computing in Civil Engineering 23, no. 2 (2009): 119–27. http://dx.doi.org/10.1061/(asce)0887-3801(2009)23:2(119).

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Lee, Jay H., and Wee Chin Wong. "Approximate dynamic programming approach for process control." IFAC Proceedings Volumes 42, no. 11 (2009): 26–35. http://dx.doi.org/10.3182/20090712-4-tr-2008.00006.

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McGrew, James S., Jonathon P. How, Brian Williams, and Nicholas Roy. "Air-Combat Strategy Using Approximate Dynamic Programming." Journal of Guidance, Control, and Dynamics 33, no. 5 (2010): 1641–54. http://dx.doi.org/10.2514/1.46815.

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Dissertations / Theses on the topic "Approximate dynamic programming"

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Sadiq, Mohammad. "Approximate Dynamic Programming Methods in HEVs." Thesis, KTH, Maskinkonstruktion (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-182762.

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Hybrid Electric Vehicles (HEV) have been gaining popularity worldwide for their efficient fuel consumption and therefore an overall reduction in the oil demand. This greatly benefits the environment since this leads to lesser emissions and hence lower greenhouse effect. Therefore research in this field is very active with a demand for new and better fuel consumption strategies. Many different methods for the energy management of HEV are being used, one particular method which promises global optimality is Dynamic Programming. Dynamic Programming yields a global optimum results but suffers from
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Vyzas, Elias. "Approximate dynamic programming for some queueing problems." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10282.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997, and Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering, 1997.<br>Includes bibliographical references (p. 81-82).<br>by Elias Vyzas.<br>M.S.
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Sauré, Antoine. "Approximate dynamic programming methods for advance patient scheduling." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43448.

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This dissertation studies an advance multi-priority patient scheduling problem. Patrick et al. (2008) formulated a version of this problem as a discounted infinite-horizon Markov decision process (MDP) and studied it using a linear programming method based on an affine value function approximation. This thesis starts by presenting an alternative solution approach for this problem based on the use of simulation, a policy iteration framework and a non-linear value function approximation. It then extends the dynamic multi-priority patient scheduling model and solution approach developed by Patric
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Child, Christopher H. T. "Approximate dynamic programming with parallel stochastic planning operators." Thesis, City University London, 2011. http://openaccess.city.ac.uk/1109/.

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This thesis presents an approximate dynamic programming (ADP) technique for environment modelling agents. The agent learns a set of parallel stochastic planning operators (P-SPOs) by evaluating changes in its environment in response to actions, using an association rule mining approach. An approximate policy is then derived by iteratively improving state value aggregation estimates attached to the operators using the P-SPOs as a model in a Dyna-Q-like architecture. Reinforcement learning and dynamic programming are powerful techniques for automated agent decision making in stochastic environme
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Liu, Ning. "Approximate dynamic programming algorithms for production-planning problems." Thesis, Wichita State University, 2013. http://hdl.handle.net/10057/10636.

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The capacitated lot-sizing problem (CLSP) is a core problem for successfully reducing overall costs in any production process. The exact approaches proposed for solving the CLSP are based on two major methods: mixed-integer programming and dynamic programming. This thesis provides a new idea for approximating the inventory cost function to be used in a truncated dynamic program for solving the CLSP. In the proposed method, by using only a partial dynamic process, the inventory cost function is approximated, and then the resulting approximate cost function is used as a value function in each st
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Demir, Ramazan. "An approximate dynamic programming approach to discrete optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9137.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.<br>Includes bibliographical references (leaves 181-189).<br>We develop Approximate Dynamic Programming (ADP) methods to integer programming problems. We describe and investigate parametric, nonparametric and base-heuristic learning approaches to approximate the value function in order to break the curse of dimensionality. Through an extensive computational study we illustrate that our ADP approach to integer programming competes successfully with existing methodologies including
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Cai, C. "Adaptive traffic signal control using approximate dynamic programming." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20164/.

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This thesis presents a study on an adaptive traffic signal controller for real-time operation. An approximate dynamic programming (ADP) algorithm is developed for controlling traffic signals at isolated intersection and in distributed traffic networks. This approach is derived from the premise that classic dynamic programming is computationally difficult to solve, and approximation is the second-best option for establishing sequential decision-making for complex process. The proposed ADP algorithm substantially reduces computational burden by using a linear approximation function to replace th
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Nadarajah, Selvaprabu. "Approximate Dynamic Programming for Commodity and Energy Merchant Operations." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/350.

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We study the merchant operations of commodity and energy conversion assets. Examples of such assets include natural gas pipelines systems, commodity swing options, and power plants. Merchant operations involves managing these assets as real options on commodity and energy prices with the objective of maximizing the market value of these assets. The economic relevance of natural gas conversion assets has increased considerably since the occurrence of the oil and gas shale boom; for example, the Energy Information Agency expects natural gas to be the source of 30% of the world's electricity prod
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Bethke, Brett (Brett M. ). "Kernel-based approximate dynamic programming using Bellman residual elimination." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57544.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 207-221).<br>Many sequential decision-making problems related to multi-agent robotic systems can be naturally posed as Markov Decision Processes (MDPs). An important advantage of the MDP framework is the ability to utilize stochastic system models
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Valenti, Mario J. (Mario James) 1976. "Approximate dynamic programming with applications in multi-agent systems." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40330.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>MIT Institute Archives copy: contains CDROM of thesis in .pdf format.<br>Includes bibliographical references (p. 151-161).<br>This thesis presents the development and implementation of approximate dynamic programming methods used to manage multi-agent systems. The purpose of this thesis is to develop an architectural framework an
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Books on the topic "Approximate dynamic programming"

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Powell, Warren B. Approximate Dynamic Programming. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118029176.

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Ulmer, Marlin Wolf. Approximate Dynamic Programming for Dynamic Vehicle Routing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55511-9.

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Powell, Warren B. Approximate dynamic programming: Solving the curses of dimensionality. 2nd ed. J. Wiley & Sons, 2011.

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Approximate dynamic programming: Solving the curses of dimensionality. J. Wiley & Sons, 2007.

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Powell, Warren B. Approximate dynamic programming: Solving the curses of dimensionality. Wiley-Interscience, 2007.

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Lewis, Frank L., and Derong Liu, eds. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118453988.

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Thomas, L. C. Approximate solutions of moving target search problems using dynamic programming. University of Edinburgh. Department of Business Studies, 1987.

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IEEE, International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. IEEE, 2007.

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IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. IEEE, 2007.

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IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. IEEE, 2007.

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Book chapters on the topic "Approximate dynamic programming"

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Fu, Michael C. "Approximate Dynamic Programming." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1189.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Approximate Dynamic Programming." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_26.

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Kamalapurkar, Rushikesh, Patrick Walters, Joel Rosenfeld, and Warren Dixon. "Approximate Dynamic Programming." In Communications and Control Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78384-0_2.

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Munos, Rémi. "Approximate Dynamic Programming." In Markov Decision Processes in Artificial Intelligence. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557426.ch3.

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Werbos, Paul J. "Approximate Dynamic Programming (ADP)." In Encyclopedia of Systems and Control. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_100096-1.

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Werbos, Paul J. "Approximate Dynamic Programming (ADP)." In Encyclopedia of Systems and Control. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100096.

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Jiang, Yu, and Zhong-Ping Jiang. "Robust Adaptive Dynamic Programming." In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch13.

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Seiffertt, John, and Donald C. Wunsch. "Approximate Dynamic Programming on Time Scales." In Evolutionary Learning and Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-03180-9_5.

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Powell, Warren B., and Ilya O. Ryzhov. "Optimal Learning and Approximate Dynamic Programming." In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118453988.ch18.

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Veksler, Olga. "Dynamic Programming for Approximate Expansion Algorithm." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_61.

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Conference papers on the topic "Approximate dynamic programming"

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Dyer, Martin. "Approximate counting by dynamic programming." In the thirty-fifth ACM symposium. ACM Press, 2003. http://dx.doi.org/10.1145/780542.780643.

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O'Donoghue, Brendan, Yang Wang, and Stephen Boyd. "Min-max approximate dynamic programming." In Control (MSC). IEEE, 2011. http://dx.doi.org/10.1109/cacsd.2011.6044538.

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Summers, Tyler H., Konstantin Kunz, Nikolaos Kariotoglou, Maryam Kamgarpour, Sean Summers, and John Lygeros. "Approximate dynamic programming via sum of squares programming." In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669374.

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Preux, Philippe, Sertan Girgin, and Manuel Loth. "Feature discovery in approximate dynamic programming." In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). IEEE, 2009. http://dx.doi.org/10.1109/adprl.2009.4927533.

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Wang, Lin, Hui Peng, Hua-yong Zhu, and Lin-cheng Shen. "A Survey of Approximate Dynamic Programming." In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2009. http://dx.doi.org/10.1109/ihmsc.2009.222.

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Deisenroth, Marc P., Jan Peters, and Carl E. Rasmussen. "Approximate dynamic programming with Gaussian processes." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4587201.

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Kariotoglou, Nikolaos, Sean Summers, Tyler Summers, Maryam Kamgarpour, and John Lygeros. "Approximate dynamic programming for stochastic reachability." In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669603.

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Zhao, Dongbin, Jianqiang Yi, and Derong Liu. "Particle Swarn Optimized Adaptive Dynamic Programming." In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. http://dx.doi.org/10.1109/adprl.2007.368166.

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Atkeson, Christopher G. "Randomly Sampling Actions In Dynamic Programming." In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. http://dx.doi.org/10.1109/adprl.2007.368187.

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Sutter, Tobias, Angeliki Kamoutsi, Peyman Mohajerin Esfahani, and John Lygeros. "Data-driven approximate dynamic programming: A linear programming approach." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264426.

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Reports on the topic "Approximate dynamic programming"

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Nachmani, Gil. Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada475720.

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