Feryal M. P. Behbahani

According to our database, Feryal M. P. Behbahani authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Training Language Models to Self-Correct via Reinforcement Learning.

Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, Yi Su, John D. Co-Reyes, Avi Singh, Kate Baumli, Shariq Iqbal, Colton Bishop, Rebecca Roelofs, Lei M. Zhang, Kay McKinney, Disha Shrivastava, Cosmin Paduraru, George Tucker, Doina Precup, Feryal M. P. Behbahani, Aleksandra Faust

CoRR, 2024

Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

CoRR, 2024

Many-Shot In-Context Learning.

Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle

CoRR, 2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Vision-Language Models as a Source of Rewards.

Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang

CoRR, 2023

Hierarchical Reinforcement Learning in Complex 3D Environments.

Bernardo Ávila Pires, Feryal M. P. Behbahani, Hubert Soyer, Kyriacos Nikiforou, Thomas Keck, Satinder Singh

CoRR, 2023

Human-Timescale Adaptation in an Open-Ended Task Space.

Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal M. P. 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

CoRR, 2023

Structured State Space Models for In-Context Reinforcement Learning.

Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob N. Foerster, Satinder Singh, Feryal M. P. Behbahani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Human-Timescale Adaptation in an Open-Ended Task Space.

Jakob Bauer, Kate Baumli, Feryal M. P. 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, Satinder Singh, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, Lei M. Zhang

Proceedings of the International Conference on Machine Learning, 2023

Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality.

Tom Zahavy, Yannick Schroecker, Feryal M. P. Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Analysing deep reinforcement learning agents trained with domain randomisation.

Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath

Neurocomputing, 2022

Model-Value Inconsistency as a Signal for Epistemic Uncertainty.

Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero

Proceedings of the International Conference on Machine Learning, 2022

2021

On the role of planning in model-based deep reinforcement learning.

Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning Compositional Neural Programs for Continuous Control.

Thomas Pierrot, Nicolas Perrin, Feryal M. P. Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Privileged Information Dropout in Reinforcement Learning.

Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal M. P. Behbahani, Wendelin Boehmer, Shimon Whiteson

CoRR, 2020

Modular Meta-Learning with Shrinkage.

Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation.

Tianhong Dai, Kai Arulkumaran, Samyakh Tukra, Feryal M. P. Behbahani, Anil Anthony Bharath

CoRR, 2019

Learning From Demonstration in the Wild.

Feryal M. P. Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin, Sudhanshu Kasewa, Ciprian Stirbu, João Gomes, Supratik Paul, Frans A. Oliehoek, João V. Messias, Shimon Whiteson

Proceedings of the International Conference on Robotics and Automation, 2019

2018

Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ.

Valliappa Chockalingam, Tegg Tae Kyong Sung, Feryal M. P. Behbahani, Rishab Gargeya, Amlesh Sivanantham, Aleksandra Malysheva

Proceedings of the Joint Proceedings of the AIIDE 2018 Workshops co-located with 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2018), 2018

2016

Reverse-engineering the visual and haptic perceptual algorithms in the brain.

Feryal M. P. Behbahani

PhD thesis, 2016

Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics.

Feryal M. P. Behbahani, Guillem Singla-Buxarrais, A. Aldo Faisal

Proceedings of the Haptics: Perception, Devices, Control, and Applications, 2016

2015

Haptic SLAM for context-aware robotic hand prosthetics - simultaneous inference of hand pose and object shape using particle filters.

Feryal M. P. Behbahani, Ruth Taunton, Andreas A. C. Thomik, A. Aldo Faisal

Proceedings of the 7th International IEEE/EMBS Conference on Neural Engineering, 2015