no code implementations • 27 Nov 2023 • Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin Riedmiller, Nicolas Heess, Markus Wulfmeier
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL).
no code implementations • 16 Nov 2023 • Li Kevin Wenliang, Ben Moran
These observations suggest that SBMs can flexibly mix samples with the learned score field while carefully maintaining a manifold-like structure of the data distribution.
no code implementations • 26 Apr 2023 • Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments.
no code implementations • 24 Nov 2022 • Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin Riedmiller
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents.
no code implementations • 10 Oct 2022 • Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee, Francesco Nori, Tuomas Haarnoja, Ben Moran, Steven Bohez, Fereshteh Sadeghi, Bojan Vujatovic, Nicolas Heess
A simulation is then created using the rendering engine in a physics simulator which computes contact dynamics from the static scene geometry (estimated from the NeRF volume density) and the dynamic objects' geometry and physical properties (assumed known).
no code implementations • 31 Mar 2022 • Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel, Fereshteh Sadeghi, Leonard Hasenclever, Yuval Tassa, Emilio Parisotto, Jan Humplik, Tuomas Haarnoja, Roland Hafner, Markus Wulfmeier, Michael Neunert, Ben Moran, Noah Siegel, Andrea Huber, Francesco Romano, Nathan Batchelor, Federico Casarini, Josh Merel, Raia Hadsell, Nicolas Heess
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots.