Zobrazeno 1 - 10
of 507
pro vyhledávání: '"Reed, Scott"'
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:
Bousmalis, Konstantinos, Vezzani, Giulia, Rao, Dushyant, Devin, Coline, Lee, Alex X., Bauza, Maria, Davchev, Todor, Zhou, Yuxiang, Gupta, Agrim, Raju, Akhil, Laurens, Antoine, Fantacci, Claudio, Dalibard, Valentin, Zambelli, Martina, Martins, Murilo, Pevceviciute, Rugile, Blokzijl, Michiel, Denil, Misha, Batchelor, Nathan, Lampe, Thomas, Parisotto, Emilio, Żołna, Konrad, Reed, Scott, Colmenarejo, Sergio Gómez, Scholz, Jon, Abdolmaleki, Abbas, Groth, Oliver, Regli, Jean-Baptiste, Sushkov, Oleg, Rothörl, Tom, Chen, José Enrique, Aytar, Yusuf, Barker, Dave, Ortiz, Joy, Riedmiller, Martin, Springenberg, Jost Tobias, Hadsell, Raia, Nori, Francesco, Heess, Nicolas
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learning. Inspired by recent advances in foundation models for vision and lan
Externí odkaz:
http://arxiv.org/abs/2306.11706
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
Autor:
Ortega, Pedro A., Kunesch, Markus, Delétang, Grégoire, Genewein, Tim, Grau-Moya, Jordi, Veness, Joel, Buchli, Jonas, Degrave, Jonas, Piot, Bilal, Perolat, Julien, Everitt, Tom, Tallec, Corentin, Parisotto, Emilio, Erez, Tom, Chen, Yutian, Reed, Scott, Hutter, Marcus, de Freitas, Nando, Legg, Shane
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains. One important problem class that has remained relatively elusive h
Externí odkaz:
http://arxiv.org/abs/2110.10819
Autor:
Konyushkova, Ksenia, Zolna, Konrad, Aytar, Yusuf, Novikov, Alexander, Reed, Scott, Cabi, Serkan, de Freitas, Nando
In offline reinforcement learning (RL) agents are trained using a logged dataset. It appears to be the most natural route to attack real-life applications because in domains such as healthcare and robotics interactions with the environment are either
Externí odkaz:
http://arxiv.org/abs/2012.06899
Autor:
Zolna, Konrad, Novikov, Alexander, Konyushkova, Ksenia, Gulcehre, Caglar, Wang, Ziyu, Aytar, Yusuf, Denil, Misha, de Freitas, Nando, Reed, Scott
Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. However, BC does not effectively leverage what we will refer to as unlabele
Externí odkaz:
http://arxiv.org/abs/2011.13885
Autor:
Wang, Ziyu, Novikov, Alexander, Zolna, Konrad, Springenberg, Jost Tobias, Reed, Scott, Shahriari, Bobak, Siegel, Noah, Merel, Josh, Gulcehre, Caglar, Heess, Nicolas, de Freitas, Nando
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and sa
Externí odkaz:
http://arxiv.org/abs/2006.15134
Autor:
Zolna, Konrad, Reed, Scott, Novikov, Alexander, Colmenarejo, Sergio Gomez, Budden, David, Cabi, Serkan, Denil, Misha, de Freitas, Nando, Wang, Ziyu
We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. When the discriminator focuses on task-irrelevant features, it does not
Externí odkaz:
http://arxiv.org/abs/1910.01077
Autor:
Cabi, Serkan, Colmenarejo, Sergio Gómez, Novikov, Alexander, Konyushkova, Ksenia, Reed, Scott, Jeong, Rae, Zolna, Konrad, Aytar, Yusuf, Budden, David, Vecerik, Mel, Sushkov, Oleg, Barker, David, Scholz, Jonathan, Denil, Misha, de Freitas, Nando, Wang, Ziyu
Publikováno v:
Robotics: Science and Systems Conference 2020
We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions. We show how to apply this framework to accomplish three different object manipu
Externí odkaz:
http://arxiv.org/abs/1909.12200
Autor:
Pierrot, Thomas, Ligner, Guillaume, Reed, Scott, Sigaud, Olivier, Perrin, Nicolas, Laterre, Alexandre, Kas, David, Beguir, Karim, de Freitas, Nando
We propose a novel reinforcement learning algorithm, AlphaNPI, that incorporates the strengths of Neural Programmer-Interpreters (NPI) and AlphaZero. NPI contributes structural biases in the form of modularity, hierarchy and recursion, which are help
Externí odkaz:
http://arxiv.org/abs/1905.12941