We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
— In this paper, a distributed power control algorithm is proposed for wireless relay networks in interference-limited environments. The objective is to minimize the total transm...
This paper describes initial testing of a novel idea to combine a CGP with an EDA. In recent work a new improved crossover technique was successfully applied to a CGP. To implemen...
— In this paper we present a novel approach to robust visual servoing. This method removes the feature tracking step from a typical visual servoing algorithm. We do not need corr...