Feryal Behbahani

Publication Activity (10 Years)

Years Active: 2018-2022
Publications (10 Years): 15

Top Topics

Global Optimality

Partial Observability

Privileged Information

Reinforcement Learning

Top Venues

Publications

Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Analysing deep reinforcement learning agents trained with domain randomisation. Neurocomputing 493 (2022)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML (2022)
Tom Zahavy, Yannick Schroecker, Feryal Behbahani, Kate Baumli, Sebastian Flennerhag, Shaobo Hou, Satinder Singh
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality. CoRR (2022)
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber
On the role of planning in model-based deep reinforcement learning. ICLR (2021)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR (2021)
Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani, Wendelin Boehmer, Shimon Whiteson
Privileged Information Dropout in Reinforcement Learning. CoRR (2020)
Thomas Pierrot, Nicolas Perrin, Feryal Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas
Learning Compositional Neural Programs for Continuous Control. CoRR (2020)
Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas
Modular Meta-Learning with Shrinkage. NeurIPS (2020)
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal 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
Acme: A Research Framework for Distributed Reinforcement Learning. CoRR (2020)
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber
On the role of planning in model-based deep reinforcement learning. CoRR (2020)
Feryal Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin, Sudhanshu Kasewa, Ciprian Stirbu, João Gomes, Supratik Paul, Frans A. Oliehoek, João V. Messias, Shimon Whiteson
Learning From Demonstration in the Wild. ICRA (2019)
Tianhong Dai, Kai Arulkumaran, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation. CoRR (2019)
Yutian Chen, Abram L. Friesen, Feryal Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas
Modular Meta-Learning with Shrinkage. CoRR (2019)
Valliappa Chockalingam, Tegg Tae Kyong Sung, Feryal Behbahani, Rishab Gargeya, Amlesh Sivanantham, Aleksandra Malysheva
Extending World Models for Multi-Agent Reinforcement Learning in MALMÖ. AIIDE Workshops (2018)
Feryal Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin, Sudhanshu Kasewa, Ciprian Stirbu, João Gomes, Supratik Paul, Frans A. Oliehoek, João V. Messias, Shimon Whiteson
Learning from Demonstration in the Wild. CoRR (2018)