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
  6. Reports

Academic literature on the topic 'Approximate dynamic programming'

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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 (February 7, 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 (January 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 stakeholders. The primary initiative is to strike a balance between airspace capacity and air traffic demand. Imbalances in capacity and demand are resolved by initiatives such as the ground delay program and rerouting, often resulting in systemwide delays. This paper, a proof of concept for the dynamic programming approach to dynamic airspace configuration by static forming of sectors, addresses static forming of sectors by partitioning airspace according to controller workload. The paper applies the dynamic programming technique to generate sectors in the Fort Worth, Texas, Air Route Traffic Control Center; compares it with current sectors; and lays a foundation for future work. Initial results of the dynamic programming methodology are promising in terms of sector shapes and the number of sectors that are comparable to current operations.
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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 (December 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 (December 1, 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 episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy. This setting is quite flexible and can incorporate easily initial available data and grouped questions.
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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 (January 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 (May 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 (February 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 (March 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 (September 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 high computation cost. Among the different methods to counter this curse of dimensionality one of the popular is Approximate Dynamic Programming (ADP). This thesis investigates the literature on the different methods of ADP applied to HEV and an implementation showing a reduction in the computation time for a specific HEV energy management problem.
Elhybridfordon (HEV) har ökat i popularitet över hela världen pga sin låga bränsleförbrukning, vilket har minskat efterfrågan på olja. Detta gynnar i hög grad miljön, eftersom detta leder till mindre utsläpp och följaktligen en lägre växthuseffekt. Därför pågår aktiv forskning inom detta område med en ökad efterfrågan på nya och bättre strategier för bränsleförbrukning. Många olika metoder för energihantering av HEV använder en särskild metod; dynamisk programmering. Dynamisk programmering ger ett optimalt globalt resultat men på bekostnad av längre beräkningstider. Den mest använda metoden för att motverka denna typ av problematik i högdimensionella system är Approximate Dynamic Programming (ADP). Denna avhandling undersöker och beskriver litteraturen på de olika metoderna för ADP tillämpade på HEV samt en genomförandefas som visar en minskning av beräkningstiden för ett HEV-problem gällande energihantering.
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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.
Includes bibliographical references (p. 81-82).
by Elias Vyzas.
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 Patrick et al. by considering patients who receive service across multiple days and for irregular lengths of time, and by allowing the possibility of using overtime on different days of the booking horizon. The research described in this dissertation is based on the hypothesis that some patients can be booked further into the future allowing the appointments for urgent patients to be scheduled earlier, and it seeks to identify effective policies for allocating available service capacity to incoming demand while reducing patient wait times in a cost-effective manner. Through the use of approximate dynamic programming techniques, it illustrates the importance of adequately assessing the future impact of today's decisions in order to more intelligently allocate capacity. Chapter 1 provides an overview of the multi-priority patient scheduling problem and a review of the literature relevant to it. Chapter 2 describes a simulation-based algorithm for solving a version of this problem and compares the performance of the resulting appointment scheduling policies against the performance of four other policies, including the one derived from the linear programming method. Chapter 3 extends the dynamic multi-priority patient scheduling model and solution approach developed by Patrick et al. It presents a discounted infinite-horizon MDP model for scheduling cancer treatments in radiation therapy units and a linear programming method for solving it. The benefits from the proposed method are evaluated by simulating its performance for a practical example based on data provided by the British Columbia Cancer Agency. Chapter 4 describes a teaching tool developed to illustrate advance patient scheduling practices to health care professionals and students. Finally, this dissertation concludes with additional discussion, extensions and further applications.
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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 environments. Dynamic programming is effective when there is a known environment model, while reinforcement learning is effective when a model is not available. The techniques derive a policy: a mapping from each environment state to an action which optimizes the long term reward the agent receives. The standard methods become less effective as the state space for the environment increases because they require values to be associated with each state, the storage and processing of which is exponential to the number of state variables. Resolving this “curse of dimensionality” is an important topic of research amongst all communities working on this problem. Two key methods are to: (i) derive an estimate of the value (approximate dynamic programming) using function approximation or state aggregation; or (ii) build a model of the environment from experience. This thesis presents a method of combining these approaches by exploiting structure in the state transition and value functions captured in a set of planning operators which are learnt through experience in the environment. Standard planning operators define the deterministic changes that occur in an environment in response to an action. This work presents Parallel Stochastic Planning Operators (P-SPOs), a novel form of planning operator providing a structured model of the state transition function in environments which are both non-deterministic and for which changes can occur outside the influence of actions. Next, an automated method for extracting P-SPOs from observations in an environment is explored using an adaptation of association rule mining. Finally, methods of relating the state transition structure encapsulated in the P-SPOs to state values, using the operators to store state value aggregation estimates, are evaluated. The framework described provides a method by which approximate dynamic programming can be applied by designers of AI agents and AI planning systems for which they have minimal prior knowledge. The framework and P-SPO based implementations are tested against standard techniques in two bench-mark stochastic environments: a “slippery gripper” block painting robot; and a “predator-prey” agent environment. Experimental results show that an agent using a P-SPO-based approach is able to learn an accurate model of its environment if successor state variables exhibit conditional independence, and an approximate model in the non-independent case. Results also demonstrate that the agent’s ability to generalise to previously unseen states using the model allow it to form an improved policy over an agent employing a standard Dyna-Q based technique. Finally, an approximate policy stored in state aggregation estimates attached to operators is shown to be optimal in experiments for which the P-SPO set contains sufficient information for effective aggregations to be formed.
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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 stage of the approximate dynamic program. In this thesis, six different algorithms are developed for the CLSP, based on three different types of approximate dynamic programming approaches. The general methodology combines dynamic programming with data fitting and approximation techniques to estimate the inventory cost function at each stage of the dynamic program. Furthermore, three main algorithmic frameworks to compute a piecewise linear approximate inventory cost function for the CLSP are provided. The first approach integrates regression models into an approximate dynamic program. The second approach uses the information obtained by a partial dynamic process to approximate the piecewise linear inventory cost function. The third approach uses slope-check and bisection techniques to locate the breakpoints of the piecewise linear function in order to approximate the inventory cost function for the CLSP. The effectiveness of the proposed methods are analyzed on various types of CLSP instances with different cost and capacity characteristics. Computational results show that approximation approaches could considerably decrease the computational time required by the dynamic program and the integer program for different CLSP instances. Furthermore, in most cases, some of the proposed approaches can accurately capture the optimal solution of the problem.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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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.
Includes bibliographical references (leaves 181-189).
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 state of art commercial packages like CPLEX. Our benchmarks for comparison are solution quality, running time and robustness (i.e., small deviations in the computational resources such as running time for varying instances of same size). In this thesis, we particularly focus on knapsack problems and the binary integer programming problem. We explore an integrated approach to solve discrete optimization problems by unifying optimization techniques with statistical learning. Overall, this research illustrates that the ADP is a promising technique by providing near-optimal solutions within reasonable amount of computation time especially for large scale problems with thousands of variables and constraints. Thus, Approximate Dynamic Programming can be considered as a new alternative to existing approximate methods for discrete optimization problems.
by Ramazan Demir.
Ph.D.
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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 the exact value function of dynamic programming solution. Machine-learning techniques are used to improve the approximation progressively. Not knowing the ideal response for the approximation to learn from, we use the paradigm of unsupervised learning, and reinforcement learning in particular. Temporal-difference learning and perturbation learning are investigated as appropriate candidates in the family of unsupervised learning. We find in computer simulation that the proposed method achieves substantial reduction in vehicle delays in comparison with optimised fixed-time plans, and is competitive against other adaptive methods in computational efficiency and effectiveness in managing varying traffic. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised. The proposed ADP algorithm is in compliance with a range of discrete systems of resolution from 0.5 to 5 seconds per temporal step. This study demonstrates the readiness of the proposed approach for real-time operations at isolated intersections and the potentials for distributed network control.
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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 production by 2040 and the McKinsey Global Institute projects United States spending on energy infrastructure to be about 100 Billion dollars by 2020. Managing commodity and energy conversion assets can be formulated as intractable Markov decision problems (MDPs), especially when using high dimensional price models commonly employed in practice. We develop approximate dynamic programming (ADP) methods for computing near optimal policies and lower and upper bounds on the market value of these assets. We focus on overcoming issues with the standard math programming and financial engineering ADP methods, that is, approximate linear programing (ALP) and least squares Monte Carlo (LSM), respectively. In particular, we develop: (i) a novel ALP relaxation framework to improve the ALP approach and use it to derive two new classes of ALP relaxations; (ii) an LSM variant in the context of popular practice-based price models to alleviate the substantial computational overhead when estimating upper bounds on the market value using existing LSM variants; and (iii) a mixed integer programming based ADP method that is exact with respect to a policy performance measure, while methods in the literature are heuristic in nature. Computational experiments on realistic instances of natural gas storage and crude oil swing options show that both our ALP relaxations and LSM methods are efficient and deliver near optimal policies and tight lower and upper bounds. Our LSM variant is also between one and three orders of magnitude faster than existing LSM variants for estimating upper bounds. Our mixed integer programming ADP model is computationally expensive to solve but its exact nature motivates further research into its solution. We provide theoretical support for our methods: By deriving bounds on approximation error we establish the optimality of our best ALP relaxation class in limiting regimes of practical relevance and provide a theoretical perspective on the relative performance of our LSM variant and existing LSM variants. We also unify different ADP methods in the literature using our ALP relaxation framework, including the financial engineering based LSM method. In addition, we employ ADP to study the novel application of jointly managing storage and transport assets in a natural gas pipeline system; the literature studies these assets in isolation. We leverage our structural analysis of the optimal storage policy to extend an LSM variant for this problem. This extension computes near optimal policies and tight bounds on instances formulated in collaboration with a major natural gas trading company. We use our extension and these instances to answer questions relevant to merchants managing such assets. Overall, our findings highlight the role of math programming for developing ADP methods. Although we focus on managing commodity and energy conversion assets, the techniques in this thesis have potential broader relevance for solving MDPs in other application contexts, such as inventory control with demand forecast updating, multiple sourcing, and optimal medical treatment design.
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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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 207-221).
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, thereby allowing the system to make sound decisions even if there is randomness in the system evolution over time. Unfortunately, the curse of dimensionality prevents most MDPs of practical size from being solved exactly. One main focus of the thesis is on the development of a new family of algorithms for computing approximate solutions to large-scale MDPs. Our algorithms are similar in spirit to Bellman residual methods, which attempt to minimize the error incurred in solving Bellman's equation at a set of sample states. However, by exploiting kernel-based regression techniques (such as support vector regression and Gaussian process regression) with nondegenerate kernel functions as the underlying cost-to-go function approximation architecture, our algorithms are able to construct cost-to-go solutions for which the Bellman residuals are explicitly forced to zero at the sample states. For this reason, we have named our approach Bellman residual elimination (BRE). In addition to developing the basic ideas behind BRE, we present multi-stage and model-free extensions to the approach. The multistage extension allows for automatic selection of an appropriate kernel for the MDP at hand, while the model-free extension can use simulated or real state trajectory data to learn an approximate policy when a system model is unavailable.
(cont.) We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in the limit of sampling the entire state space, and show computational results on several benchmark problems. Another challenge in implementing control policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System performance can suer if the model used to compute the policy differs from the true model. To address this challenge, we develop an adaptive architecture that allows for online MDP model learning and simultaneous re-computation of the policy. As a result, the adaptive architecture allows the system to continuously re-tune its control policy to account for better model information 3 obtained through observations of the actual system in operation, and react to changes in the model as they occur. Planning in complex, large-scale multi-agent robotic systems is another focus of the thesis. In particular, we investigate the persistent surveillance problem, in which one or more unmanned aerial vehicles (UAVs) and/or unmanned ground vehicles (UGVs) must provide sensor coverage over a designated location on a continuous basis. This continuous coverage must be maintained even in the event that agents suer failures over the course of the mission. The persistent surveillance problem is pertinent to a number of applications, including search and rescue, natural disaster relief operations, urban traffic monitoring, etc.
(cont.) Using both simulations and actual flight experiments conducted in the MIT RAVEN indoor flight facility, we demonstrate the successful application of the BRE algorithms and the adaptive MDP architecture in achieving high mission performance despite the random occurrence of failures. Furthermore, we demonstrate performance benefits of our approach over a deterministic planning approach that does not account for these failures.
by Brett M. Bethke.
Ph.D.
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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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
MIT Institute Archives copy: contains CDROM of thesis in .pdf format.
Includes bibliographical references (p. 151-161).
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 and theoretical methods that enable an autonomous mission system to manage real-time multi-agent operations. To meet this goal, we begin by discussing aspects of the real-time multi-agent mission problem. Next, we formulate this problem as a Markov Decision Process (MDP) and present a system architecture designed to improve mission-level functional reliability through system self-awareness and adaptive mission planning. Since most multi-agent mission problems are computationally difficult to solve in real-time, approximation techniques are needed to find policies for these large-scale problems. Thus, we have developed theoretical methods used to find feasible solutions to large-scale optimization problems. More specifically, we investigate methods designed to automatically generate an approximation to the cost-to-go function using basis functions for a given MDP. Next, these these techniques are used by an autonomous mission system to manage multi-agent mission scenarios. Simulation results using these methods are provided for a large-scale mission problem. In addition, this thesis presents the implementation of techniques used to manage autonomous unmanned aerial vehicles (UAVs) performing persistent surveillance operations. We present an indoor multi-vehicle testbed called RAVEN (Real-time indoor Autonomous Vehicle test ENvironment) that was developed to study long-duration missions in a controlled environment.
(cont.) The RAVEN's design allows researchers to focus on high-level tasks by autonomously managing the platform's realistic air and ground vehicles during multi-vehicle operations, thus promoting the rapid prototyping of UAV technologies by flight testing new vehicle configurations and algorithms without redesigning vehicle hardware. Finally, using the RAVEN, we present flight test results from autonomous, extended mission tests using the technologies developed in this thesis. Flight results from a 24 hr, fully-autonomous air vehicle flight-recharge test and an autonomous, multi-vehicle extended mission test using small, electric-powered air vehicles are provided.
by Mario J. Valenti.
Ph.D.
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Books on the topic "Approximate dynamic programming"

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Powell, Warren B. Approximate Dynamic Programming. Hoboken, NJ, USA: 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. Cham: 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. Hoboken, N.J: J. Wiley & Sons, 2011.

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

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

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Lewis, Frank L., and Derong Liu, eds. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. Hoboken, NJ, USA: 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. Edinburgh: 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. Piscataway, NJ: 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. Piscataway, NJ: 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. Piscataway, NJ: 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, 73–77. Boston, MA: 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, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Approximate Dynamic Programming." In Encyclopedia of Machine Learning, 39. Boston, MA: 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, 17–42. Cham: 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, 67–98. Hoboken, NJ USA: 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, 1–7. London: 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, 76–82. Cham: 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, 281–302. Hoboken, NJ, USA: 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, 61–76. Berlin, Heidelberg: 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, 410–31. Hoboken, NJ, USA: 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, 850–63. Berlin, Heidelberg: 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. New York, New York, USA: 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. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada475720.

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