66 citations · 176 across the 10 of their papers we have counts for
11 papers
A Generalist Dynamics Model for Control
Ingmar Schubert, Jingwei Zhang, Jake Bruce +7
We investigate the use of transformer sequence models as dynamics models (TDMs) for control. We find that TDMs exhibit strong generalization capabilities to unseen environments, bo…
Accelerating exploration and representation learning with offline pre-training
Bogdan Mazoure, Jake Bruce, Doina Precup +2
Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement learning (RL) algorithms address this chall…
A Generalist Agent
Scott Reed, Konrad Zolna, Emilio Parisotto +17
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we…
Imitation by Predicting Observations
Andrew Jaegle, Yury Sulsky, Arun Ahuja +3
Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to ob…
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…