1 code implementation • 11 Sep 2020 • Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Peter Karkus, Sébastien Racanière, Lars Buesing, Timothy Lillicrap, Nicolas Heess
To encourage progress towards this goal we introduce a set of physically embedded planning problems and make them publicly available.
no code implementations • 5 Dec 2018 • Jonathan J. Hunt, Andre Barreto, Timothy P. Lillicrap, Nicolas Heess
Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning.
no code implementations • NeurIPS 2016 • Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap
SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring $100,\! 000$s of time steps and memories.
Ranked #6 on
Question Answering
on bAbi
(Mean Error Rate metric)
no code implementations • NeurIPS 2017 • André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver
Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks.
3 code implementations • 14 Dec 2015 • Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver
Partially observed control problems are a challenging aspect of reinforcement learning.
161 code implementations • 9 Sep 2015 • Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
Ranked #2 on
OpenAI Gym
on HalfCheetah-v4