Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
Actor-Critic based approaches were among the first to address reinforcement learning in a general setting. Recently, these algorithms have gained renewed interest due to their gen...
Abstract— We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although...
Jun Morimoto, Christopher G. Atkeson, Gen Endo, Go...
One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multiobjective genetic programming (GP) all...
We present an integrated approach for reasoning about and learning conversation patterns in multiagent communication. The approach is based on the assumption that information abou...
Michael Rovatsos, Felix A. Fischer, Gerhard Wei&sz...