Journal
MATHEMATICS OF OPERATIONS RESEARCH
Volume 34, Issue 1, Pages 210-237Publisher
INFORMS
DOI: 10.1287/moor.1080.0360
Keywords
stochastic learning and adaptive control; stochastic approximation; approximate dynamic programming
Funding
- Air Force Office of Scientific Research Grant AFOSR Contract [FA9550-08-1-0195]
Ask authors/readers for more resources
We consider a multistage asset acquisition problem where assets are purchased now, at a price that varies randomly over time, to be used to satisfy a random demand at a particular point in time in the future. We provide a rare proof of convergence for an approximate dynamic programming algorithm using pure exploitation, where the states we visit depend on the decisions produced by solving the approximate problem. The resulting algorithm does not require knowing the probability distribution of prices or demands, nor does it require any assumptions about its functional form. The algorithm and its proof rely on the fact that the true value function is a family of piecewise linear concave functions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available