Approximate Dynamic Programming: Combining Regional and Local State Following Approximations

Functional Approximations and Dynamic Programming

Low-discrepancy sampling for approximate dynamic programming with local approximators

Approximate dynamic programming (ADP) relies, in the continuous-state case, on both a flexible class of models for the approximation of the value functions and a smart sampling of the state space for the numerical solution of the recursive Bellman equations. In this paper, low-discrepancy sequences, commonly employed for number-theoretic methods, are investigated as a sampling scheme in the ADP...

Approximate Dynamic Programming via Linear Programming

The curse of dimensionality gives rise to prohibitive computational requirements that render infeasible the exact solution of largescale stochastic control problems. We study an efficient method based on linear programming for approximating solutions to such problems. The approach "fits" a linear combination of preselected basis functions to the dynamic programming costtogo function. We develop...

Approximate Counting by Dynamic Programming

This proposal describes a project that is going to investigate algorithms for counting knapsack solutions and related sets, with a focus on algorithms that utilise dynamic programming. Both the feasibility of the implementation and the performance characteristics of the algorithms are going to be examined; additional goals pertain to actual algorithmic improvements, with derandomisation being t...

Approximate Dynamic Programming—ii: Algorithms

• Option Pricing. An American option allows us to buy or sell an asset at any time up to a specified time, where we make money when the price goes under or over (respectively) a set strike price. Valuing the option requires finding an optimal policy for determining when to exercise the option. • Playing Games. Computer algorithms have been designed to play backgammon, bridge, chess, and recentl...