Actively Learning Costly Reward Functions for Reinforcement Learning

1 code implementation • 23 Nov 2022 • André Eberhard, Houssam Metni, Georg Fahland, Alexander Stroh, Pascal Friederich

Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability.

Active Learning reinforcement-learning +1

Graph neural networks for materials science and chemistry

no code implementations • 5 Aug 2022 • Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e. g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials.