Results 231 to 240 of about 14,953 (260)

Approximation of Dynamic Programs

2011
Under some standard market assumptions, evaluating a derivative implies computing the discounted expected value of its future cash flows and can be written as a stochastic Dynamic Program (DP), where the state variable corresponds to the underlying assets’ observable characteristics. Approximation procedures are needed to discretize the state space and
Michèle Breton, Javier de Frutos
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Approximate dynamic programming for stochastic reachability

2013 European Control Conference (ECC), 2013
In this work we illustrate how approximate dynamic programing can be utilized to address problems of stochastic reachability in infinite state and control spaces. In particular we focus on the reach-avoid problem and approximate the value function on a linear combination of radial basis functions. In this way we get significant computational advantages
Kariotoglou, N., Summers, S., Summers, T., Kamgarpour, Maryam, Lygeros, J.   +4 more
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Approximate Dynamic Programming

2007
Preface. Acknowledgments. 1. The challenges of dynamic programming. 1.1 A dynamic programming example: a shortest path problem. 1.2 The three curses of dimensionality. 1.3 Some real applications. 1.4 Problem classes. 1.5 The many dialects of dynamic programming. 1.6 What is new in this book? 1.7 Bibliographic notes. 2. Some illustrative models.
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Approximate dynamic programming with affine ADDs

International Joint Conference on Autonomous Agents and Multiagent Systems, 2010
The Affin ADD (AADD) is an extension of the Algebraic Decision Diagram (ADD) that compactly represents context-specific, additive and multiplicative structure in functions from a discrete domain to a real-valued range. In this paper, we introduce a novel algorithm for efficientl findin AADD approximations that we use to develop the MADCAP algorithm for
Scott Sanner, William T. B. Uther, Karina Valdivia Delgado   +2 more
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Region-based approximation in approximate dynamic programming

International Journal of Control, 2022
Tohid Sardarmehni, Xingyong Song
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Revisiting Approximate Dynamic Programming and its Convergence

IEEE Transactions on Cybernetics, 2014
Value iteration-based approximate/adaptive dynamic programming (ADP) as an approximate solution to infinite-horizon optimal control problems with deterministic dynamics and continuous state and action spaces is investigated. The learning iterations are decomposed into an outer loop and an inner loop. A relatively simple proof for the convergence of the
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Approximate Dynamic Programming

2010
In any complex or large scale sequential decision making problem, there is a crucial need to use function approximation to represent the relevant functions such as the value function or the policy. The Dynamic Programming (DP) and Reinforcement Learning (RL) methods introduced in previous chapters make the implicit assumption that the value function ...
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Approximate dynamic programming for an energy-efficient parallel machine scheduling problem

European Journal of Operational Research, 2022
Mojtaba Heydar, Ryan Loxton, Elham Mardaneh   +2 more
exaly  

Function Approximation and Approximate Dynamic Programming

2022
Ashwin Rao, Tikhon Jelvis
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Nonparametric Approximate Dynamic Programming via the Kernel Method

Stochastic Systems, 2023
Ciamac Moallemi, Vivek F Farias
exaly