We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
Pedro Alejandro Ortega, Daniel Alexander Braun, Si...
The aim of this research is to develop an adaptive agent based model of auction scenarios commonly used in auction theory to help understand how competitors in auctions reach equil...
The existing reinforcement learning approaches have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. One of the typical...
Recently, industry has begun investigating and moving towards utility computing, where computational resources (processing, memory and I/O) are availably on demand at a market cos...
The problem of interest is how to dynamically allocate wireless access services in a competitive market which implements a take-it-or-leave-it allocation mechanism. In this paper ...
George Lee, Steven Bauer, Peyman Faratin, John Wro...