no code implementations • 18 May 2023 • Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
no code implementations • 3 Jun 2022 • Danny Driess, Ingmar Schubert, Pete Florence, Yunzhu Li, Marc Toussaint
This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performance of RL compared to other learned representations or even low-dimensional, hand-engineered state information.
1 code implementation • NeurIPS 2021 • Ingmar Schubert, Danny Driess, Ozgur S. Oguz, Marc Toussaint
Applications of Reinforcement Learning (RL) in robotics are often limited by high data demand.
no code implementations • 14 Jul 2021 • Ingmar Schubert, Ozgur S. Oguz, Marc Toussaint
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.
no code implementations • ICLR 2021 • Ingmar Schubert, Ozgur S Oguz, Marc Toussaint
In high-dimensional state spaces, the usefulness of Reinforcement Learning (RL) is limited by the problem of exploration.