Dong Wang, Tarek Abdelzaher, et al.
FUSION 2011
In a descending price multi-unit Dutch auction over the Internet, auctioneer gradually decrements per unit price of the item during the course of the auction. We investigate the problem of finding a decrementing price sequence that maximizes auctioneer’s total expected revenue using single-agent Reinforcement Learning. We contrast actual-return (Monte-Carlo based) learning methods with one step Q-learning and also with other adaptive strategies and report extensive comparative performance study. In our experimental design, we model bidders’ strategies in a unique way using various bid functions that capture realistic strategic behavior of bidders in such auction games. Monte-Carlo control algorithm developed here offers consistent performance and yields high average returns in all the experiments.
Dong Wang, Tarek Abdelzaher, et al.
FUSION 2011
Parul A. Mittal, Manoj Kumar, et al.
IBM J. Res. Dev
Ayushi Dalmia, J. Ganesh, et al.
WWW 2018
Jakka Sairamesh, Rakesh Mohan, et al.
IBM Systems Journal