22 citations · 77 across the 12 of their papers we have counts for
12 papers
Steering Language Models with Game-Theoretic Solvers
Ian Gemp, Roma Patel, Yoram Bachrach +6
Mathematical models of interactions among rational agents have long been studied in game theory. However these interactions are often over a small set of discrete game actions whic…
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…
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…
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the r…
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…
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
Krishan Rana, Vibhavari Dasagi, Jesse Haviland +3
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learnin…
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…
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…