Tim de Bruin — accepted AI-conference papers

2018

Integrating State Representation Learning Into Deep Reinforcement Learning

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuska

RA-L 2018

Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, whi

Cited by 119 Source ↗Scholar ↗

2016

Improved deep reinforcement learning for robotics through distribution-based experience retention

Tim de Bruin, Jens Kober, Karl Tuyls, Robert Babuška

IROS 2016poster

Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform…

Cited by 54 Source ↗Scholar ↗