22 citations · 77 across the 12 of their papers we have counts for
6 papers · 1 filter
Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team, Jakob Bauer, Kate Baumli +25
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to rein…
The Challenges of Exploration for Offline Reinforcement Learning
Nathan Lambert, Markus Wulfmeier, William Whitney +5
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal…
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Oliver Groth, Markus Wulfmeier, Giulia Vezzani +5
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the ag…
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Vibhavari Dasagi, Robert Lee, Jake Bruce +1
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robo…
Ctrl-Z: Recovering from Instability in Reinforcement Learning
Vibhavari Dasagi, Jake Bruce, Thierry Peynot +1
When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applicati…
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Vibhavari Dasagi, Robert Lee, Serena Mou +3
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-d…