Search Results for author: Feryal Behbahani

Found 9 papers, 1 papers with code

Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality

no code implementations26 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.

Model-Value Inconsistency as a Signal for Epistemic Uncertainty

no code implementations8 Dec 2021 Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero

Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.

Model-based Reinforcement Learning

Learning Compositional Neural Programs for Continuous Control

no code implementations27 Jul 2020 Thomas Pierrot, Nicolas Perrin, Feryal Behbahani, Alexandre Laterre, Olivier Sigaud, Karim Beguir, Nando de Freitas

Third, the self-models are harnessed to learn recursive compositional programs with multiple levels of abstraction.

Continuous Control

Acme: A Research Framework for Distributed Reinforcement Learning

3 code implementations1 Jun 2020 Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.

DQN Replay Dataset reinforcement-learning

Privileged Information Dropout in Reinforcement Learning

no code implementations19 May 2020 Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani, Wendelin Boehmer, Shimon Whiteson

Using privileged information during training can improve the sample efficiency and performance of machine learning systems.

reinforcement-learning

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

no code implementations18 Dec 2019 Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath

Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.

reinforcement-learning

Modular Meta-Learning with Shrinkage

no code implementations NeurIPS 2020 Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew W. Hoffman, Nando de Freitas

Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.

Image Classification Meta-Learning +2

Learning from Demonstration in the Wild

no code implementations8 Nov 2018 Feryal Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin, Sudhanshu Kasewa, Ciprian Stirbu, João Gomes, Supratik Paul, Frans A. Oliehoek, João Messias, Shimon Whiteson

Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical.

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