2.6k citations · 2.8k across the 10 of their papers we have counts for
10 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…
Offline Actor-Critic Reinforcement Learning Scales to Large Models
Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang +9
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning. We find that of…
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier +4
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional in…
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra, Ankit Anand, Jordan Hoffmann +4
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teacher policies are introduced as objectives…
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler, Felix Widmaier, Cansu Sancaktar +21
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and be…
Towards A Unified Agent with Foundation Models
Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever +3
Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and pla…