GATS: Gather-Attend-Scatter

no code implementations • 16 Jan 2024 • Konrad Zolna, Serkan Cabi, Yutian Chen, Eric Lau, Claudio Fantacci, Jurgis Pasukonis, Jost Tobias Springenberg, Sergio Gomez Colmenarejo

As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them.

Vision-Language Models as Success Detectors

no code implementations • 13 Mar 2023 • Yuqing Du, Ksenia Konyushkova, Misha Denil, Akhil Raju, Jessica Landon, Felix Hill, Nando de Freitas, Serkan Cabi

Detecting successful behaviour is crucial for training intelligent agents.

Question Answering Visual Question Answering

Flamingo: a Visual Language Model for Few-Shot Learning

5 code implementations • DeepMind 2022 • Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karen Simonyan

Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.

Few-Shot Learning Generative Visual Question Answering +11

Multimodal Few-Shot Learning with Frozen Language Models

no code implementations • NeurIPS 2021 • Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, Felix Hill

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples.

Few-Shot Learning Language Modeling +3

Semi-supervised reward learning for offline reinforcement learning

no code implementations • 12 Dec 2020 • Ksenia Konyushkova, Konrad Zolna, Yusuf Aytar, Alexander Novikov, Scott Reed, Serkan Cabi, Nando de Freitas

In offline reinforcement learning (RL) agents are trained using a logged dataset.

Behavioural cloning reinforcement-learning +2

Acme: A Research Framework for Distributed Reinforcement Learning

5 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.

Deep Reinforcement Learning DQN Replay Dataset +2

Task-Relevant Adversarial Imitation Learning

no code implementations • 2 Oct 2019 • Konrad Zolna, Scott Reed, Alexander Novikov, Sergio Gomez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang

We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels.

Imitation Learning

Scaling data-driven robotics with reward sketching and batch reinforcement learning

1 code implementation • 26 Sep 2019 • Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, Oleg Sushkov, David Barker, Jonathan Scholz, Misha Denil, Nando de Freitas, Ziyu Wang

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.

reinforcement-learning Reinforcement Learning +1

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

no code implementations • ICLR 2019 • Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators.

Learning Awareness Models

no code implementations • ICLR 2018 • Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

1 code implementation • ICLR 2018 • Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess

We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent.

Deep Reinforcement Learning Imitation Learning +2

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

no code implementations • 11 Jul 2017 • Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas

This paper introduces the Intentional Unintentional (IU) agent.

continuous-control Continuous Control

Programmable Agents

no code implementations • 20 Jun 2017 • Misha Denil, Sergio Gómez Colmenarejo, Serkan Cabi, David Saxton, Nando de Freitas

We build deep RL agents that execute declarative programs expressed in formal language.