Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Bruce, Jake"'
Autor:
Yang, Sherry, Walker, Jacob, Parker-Holder, Jack, Du, Yilun, Bruce, Jake, Barreto, Andre, Abbeel, Pieter, Schuurmans, Dale
Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, wher
Externí odkaz:
http://arxiv.org/abs/2402.17139
Autor:
Bruce, Jake, Dennis, Michael, Edwards, Ashley, Parker-Holder, Jack, Shi, Yuge, Hughes, Edward, Lai, Matthew, Mavalankar, Aditi, Steigerwald, Richie, Apps, Chris, Aytar, Yusuf, Bechtle, Sarah, Behbahani, Feryal, Chan, Stephanie, Heess, Nicolas, Gonzalez, Lucy, Osindero, Simon, Ozair, Sherjil, Reed, Scott, Zhang, Jingwei, Zolna, Konrad, Clune, Jeff, de Freitas, Nando, Singh, Satinder, Rocktäschel, Tim
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text,
Externí odkaz:
http://arxiv.org/abs/2402.15391
Autor:
Schubert, Ingmar, Zhang, Jingwei, Bruce, Jake, Bechtle, Sarah, Parisotto, Emilio, Riedmiller, Martin, Springenberg, Jost Tobias, Byravan, Arunkumar, Hasenclever, Leonard, Heess, Nicolas
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, both in a few-shot setting, where a generalist TDM is fine-tuned with sm
Externí odkaz:
http://arxiv.org/abs/2305.10912
Sequential decision-making agents struggle with long horizon tasks, since solving them requires multi-step reasoning. Most reinforcement learning (RL) algorithms address this challenge by improved credit assignment, introducing memory capability, alt
Externí odkaz:
http://arxiv.org/abs/2304.00046
Autor:
Reed, Scott, Zolna, Konrad, Parisotto, Emilio, Colmenarejo, Sergio Gomez, Novikov, Alexander, Barth-Maron, Gabriel, Gimenez, Mai, Sulsky, Yury, Kay, Jackie, Springenberg, Jost Tobias, Eccles, Tom, Bruce, Jake, Razavi, Ali, Edwards, Ashley, Heess, Nicolas, Chen, Yutian, Hadsell, Raia, Vinyals, Oriol, Bordbar, Mahyar, de Freitas, Nando
Publikováno v:
Transactions on Machine Learning Research, 11/2022, https://openreview.net/forum?id=1ikK0kHjvj
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 refer to as Gato, works as a multi-modal, multi-task, multi-embodiment
Externí odkaz:
http://arxiv.org/abs/2205.06175
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 observe behavior in the real-world, the underlying actions may not be ac
Externí odkaz:
http://arxiv.org/abs/2107.03851
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. Robotics is a significant potential application domain for many of these a
Externí odkaz:
http://arxiv.org/abs/1911.08666
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. In
Externí odkaz:
http://arxiv.org/abs/1910.03732
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 knowl
Externí odkaz:
http://arxiv.org/abs/1809.07480
Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitivel
Externí odkaz:
http://arxiv.org/abs/1807.05211