13 citations · 28 across the 5 of their papers we have counts for
8 papers
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
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…
Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
Robert Lee, Daniel Ward, Akansel Cosgun +3
Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. I…
Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer
Krishan Rana, Vibhavari Dasagi, Ben Talbot +2
Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intrac…
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…