Search Results for author: Jonathan J. Hunt

Found 6 papers, 3 papers with code

Composing Entropic Policies using Divergence Correction

no code implementations5 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.

Continuous Control reinforcement-learning

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

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)

Language Modelling Machine Translation +2

Successor Features for Transfer in Reinforcement Learning

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.

reinforcement-learning

Memory-based control with recurrent neural networks

2 code implementations14 Dec 2015 Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver

Partially observed control problems are a challenging aspect of reinforcement learning.

Continuous Control

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