284 citations · 1.2k across the 27 of their papers we have counts for
29 papers
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Mohak Bhardwaj, Thomas Lampe, Michael Neunert +6
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamic…
Towards practical reinforcement learning for tokamak magnetic control
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi +15
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbac…
Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang +16
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domai…
Slow-fast dynamics of strongly coupled adaptive frequency oscillators
Ludovic Righetti, Jonas Buchli, Auke J. Ijspeert
Oscillators have two main limitations: their synchronization properties are limited (i.e they have a finite synchronization region) and they have no memory of past interactions (i.…
Local Search for Policy Iteration in Continuous Control
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz +10
We present an algorithm for local, regularized, policy improvement in reinforcement learning (RL) that allows us to formulate model-based and model-free variants in a single framew…
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier +7
Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision…