no code implementations • 28 Feb 2023 • Bernardo Avila Pires, Feryal Behbahani, Hubert Soyer, Kyriacos Nikiforou, Thomas Keck, Satinder Singh
Hierarchical Reinforcement Learning (HRL) agents have the potential to demonstrate appealing capabilities such as planning and exploration with abstraction, transfer, and skill reuse.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • ICLR 2020 • H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Si-Qi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin Riedmiller, Matthew M. Botvinick
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.
1 code implementation • ICLR 2020 • Tom Le Paine, Caglar Gulcehre, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
no code implementations • ICLR 2019 • Avraham Ruderman, Richard Everett, Bristy Sikder, Hubert Soyer, Jonathan Uesato, Ananya Kumar, Charlie Beattie, Pushmeet Kohli
Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings.
2 code implementations • 12 Sep 2018 • Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on.
Ranked #1 on Visual Navigation on Dmlab-30
no code implementations • 13 May 2018 • Thomas Stepleton, Razvan Pascanu, Will Dabney, Siddhant M. Jayakumar, Hubert Soyer, Remi Munos
Reinforcement learning (RL) agents performing complex tasks must be able to remember observations and actions across sizable time intervals.
20 code implementations • ICML 2018 • Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.
Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)
1 code implementation • 20 Jun 2017 • Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom
Trained via a combination of reinforcement and unsupervised learning, and beginning with minimal prior knowledge, the agent learns to relate linguistic symbols to emergent perceptual representations of its physical surroundings and to pertinent sequences of actions.
7 code implementations • 17 Nov 2016 • Jane. X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, Matt Botvinick
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
1 code implementation • 11 Nov 2016 • Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J. Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent SIfre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell
Learning to navigate in complex environments with dynamic elements is an important milestone in developing AI agents.
8 code implementations • 15 Jun 2016 • Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell
Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence.
1 code implementation • 19 Dec 2014 • Hubert Soyer, Pontus Stenetorp, Akiko Aizawa
In this work, we present a novel neural network based architecture for inducing compositional crosslingual word representations.