254 citations · 292 across the 6 of their papers we have counts for
10 papers
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Residual Reinforcement Learning from Demonstrations
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a…
Learning to run a Power Network Challenge: a Retrospective Analysis
Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold +7
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand…
A Geometric Perspective on Self-Supervised Policy Adaptation
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold +1
One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what…
Model-Based Offline Planning
Arthur Argenson, Gabriel Dulac-Arnold
Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no di…
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
Caglar Gulcehre, Ziyu Wang, Alexander Novikov +15
Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to lea…