Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive genera...
My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques b...
—The purpose of this paper is to present the linkage of Thetis (a real time multi-vehicles hybrid simulator for heterogeneous vehicles) with a control architecture for the manage...
Acquiring models of the environment belongs to the fundamental tasks of mobile robots. In the past, several researchers have focused on the problem of simultaneous localization an...
In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...