no code implementations • 27 Feb 2024 • Sherry Yang, Jacob Walker, Jack Parker-Holder, Yilun Du, Jake Bruce, Andre Barreto, Pieter Abbeel, Dale Schuurmans
Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning.
no code implementations • 23 Feb 2024 • Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal Behbahani, Stephanie Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos.
no code implementations • 18 May 2023 • Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
no code implementations • 31 Mar 2023 • Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand
In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
3 code implementations • DeepMind 2022 • Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas
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.
Ranked #1 on Skill Generalization on RGB-Stacking
no code implementations • 8 Jul 2021 • Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne
Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior.
1 code implementation • 20 Nov 2019 • Vibhavari Dasagi, Robert Lee, Jake Bruce, Jürgen Leitner
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.
no code implementations • 9 Oct 2019 • Vibhavari Dasagi, Jake Bruce, Thierry Peynot, Jürgen Leitner
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 applications in safety-sensitive real-world control tasks such as robotics.
no code implementations • 20 Sep 2018 • Vibhavari Dasagi, Robert Lee, Serena Mou, Jake Bruce, Niko Sünderhauf, Jürgen Leitner
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-dimensional inputs, with little capability of incorporating prior knowledge.
1 code implementation • 28 Nov 2017 • Jake Bruce, Niko Suenderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
8 code implementations • CVPR 2018 • Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, Anton Van Den Hengel
This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
Ranked #10 on Visual Navigation on R2R