no code implementations • 18 Jan 2023 • Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei Zhang
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 reinforcement learning (RL).
no code implementations • 26 May 2022 • Tom Zahavy, Yannick Schroecker, Feryal Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations.
no code implementations • NeurIPS 2021 • Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
no code implementations • NeurIPS 2021 • Gregory Farquhar, Kate Baumli, Zita Marinho, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.
no code implementations • ICLR 2022 • DJ Strouse, Kate Baumli, David Warde-Farley, Vlad Mnih, Steven Hansen
However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective.
no code implementations • 14 Dec 2020 • Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment.
3 code implementations • 1 Jun 2020 • Matthew W. Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Nikola Momchev, Danila Sinopalnikov, Piotr Stańczyk, Sabela Ramos, Anton Raichuk, Damien Vincent, Léonard Hussenot, Robert Dadashi, Gabriel Dulac-Arnold, Manu Orsini, Alexis Jacq, Johan Ferret, Nino Vieillard, Seyed Kamyar Seyed Ghasemipour, Sertan Girgin, Olivier Pietquin, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Abe Friesen, Ruba Haroun, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.