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Approximate Dynamic Programming - Solving the Curses of Dimensionality
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Approximate dynamic programming : solving the curses of dimensionality
TLDR
This book discusses the challenges of dynamic programming, the three curses of dimensionality, and some experimental comparisons of stepsize formulas that led to the creation of ADP for online applications.
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The Knowledge-Gradient Policy for Correlated Normal Beliefs
TLDR
A fully sequential sampling policy is proposed called the knowledge-gradient policy, which is provably optimal in some special cases and has bounded suboptimality in all others and it is demonstrated how this policy may be applied to efficiently maximize a continuous function on a continuous domain while constrained to a fixed number of noisy measurements.
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Handbook of Learning and Approximate Dynamic Programming
TLDR
This chapter discusses reinforcement learning in large, high-dimensional state spaces, model-based adaptive critic designs, and applications of approximate dynamic programming in power systems control.
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Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
A Knowledge-Gradient Policy for Sequential Information Collection
TLDR
In a sequential Bayesian ranking and selection problem with independent normal populations and common known variance, a previously introduced measurement policy is studied, showing that the knowledge-gradient policy is optimal both when the horizon is a single time period and in the limit as the horizon extends to infinity.
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The Convergence of Equilibrium Algorithms with Predetermined Step Sizes
The focus of this paper is on a certain class of equilibrium traffic assignment problems characterized by a path formulation of the associated mathematical programs. In such cases the equilibration…
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An algorithm for the equilibrium assignment problem with random link times
TLDR
An equivalent minimization formulation for the traffic assignment problem when the link travel times are flow-dependent random variables and the stochastic equilibrium conditions as well as the uniqueness of the solution is offered.
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A Stochastic Formulation of the Dynamic Assignment Problem, with an Application to Truckload Motor Carriers
TLDR
A hybrid model is presented that handles the detailed assignment of drivers to loads, as well as handling forecasts of future loads, and outperforms standard myopic models that are widely used in practice.
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Solving Parallel Machine Scheduling Problems by Column Generation
TLDR
This work considers a class of problems of scheduling n jobs on m identical, uniform, or unrelated parallel machines with an objective of minimizing an additive criterion and proposes a decomposition approach for solving these problems exactly, indicating that this approach is promising and capable of solving large problems.
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