no code implementations • 13 Jun 2024 • Wilfried Bounsi, Borja Ibarz, Andrew Dudzik, Jessica B. Hamrick, Larisa Markeeva, Alex Vitvitskyi, Razvan Pascanu, Petar Veličković
Transformers have revolutionized machine learning with their simple yet effective architecture.
no code implementations • 8 Feb 2023 • Jacob Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Théophane Weber, Jessica B. Hamrick
Our results show that intrinsic exploration combined with environment models present a viable direction towards agents that are self-supervised and able to generalize to novel reward functions.
1 code implementation • 16 Jun 2022 • Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence.
no code implementations • ICLR 2022 • Ankesh Anand, Jacob Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber, Jessica B. Hamrick
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks.
Ranked #1 on Meta-Learning on ML10 (Meta-test success rate (zero-shot) metric)
no code implementations • ICLR 2021 • Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Veličković, Théophane Weber
These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
Model-based Reinforcement Learning reinforcement-learning +2
1 code implementation • 6 May 2020 • Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, Jessica B. Hamrick
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
no code implementations • 23 Apr 2020 • Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
no code implementations • ICLR 2020 • Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, Peter W. Battaglia
In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.
no code implementations • 31 Oct 2019 • Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick
We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems.
no code implementations • 5 Apr 2019 • Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick
Our results show that agents which use structured representations (e. g., objects and scene graphs) and structured policies (e. g., object-centric actions) outperform those which use less structured representations, and generalize better beyond their training when asked to reason about larger scenes.
31 code implementations • 4 Jun 2018 • Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
no code implementations • 4 Jun 2018 • Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.
no code implementations • 14 Feb 2018 • Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
We introduce $t$-\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining robot plan from the overall task goal and the observed initial $t$ actions in the plan.
no code implementations • 6 Feb 2018 • Chang Liu, Jessica B. Hamrick, Jaime F. Fisac, Anca D. Dragan, J. Karl Hedrick, S. Shankar Sastry, Thomas L. Griffiths
The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications.
no code implementations • 20 Jul 2017 • Jaime F. Fisac, Monica A. Gates, Jessica B. Hamrick, Chang Liu, Dylan Hadfield-Menell, Malayandi Palaniappan, Dhruv Malik, S. Shankar Sastry, Thomas L. Griffiths, Anca D. Dragan
In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.
1 code implementation • 7 May 2017 • Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia
The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration.
no code implementations • NeurIPS 2014 • Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Tom Griffiths
Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.