Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
On the way to the big goal - the game against the human world champion on a real soccer field - the configuration of the soccer fields in RoboCup has changed during the last yea...
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
In this article the classical self-localization approach is improved by estimating, independently from the robot’s pose, the robot’s odometric error and the landmarks’ poses....
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...