Search Results for author: Tim Harley

Found 12 papers, 4 papers with code

Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text

no code implementations19 May 2020 Felix Hill, Sona Mokra, Nathaniel Wong, Tim Harley

Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.

Language Modelling reinforcement-learning +2

Multiplicative Interactions and Where to Find Them

no code implementations ICLR 2020 Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu

We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.

Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text

no code implementations25 Sep 2019 Felix Hill, Sona Mokra, Nathaniel Wong, Tim Harley

We address this issue by integrating language encoders that are pretrained on large text corpora into a situated, instruction-following agent.

Representation Learning Transfer Learning

A Generalized Framework for Population Based Training

no code implementations5 Feb 2019 Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.

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

Asynchronous Methods for Deep Reinforcement Learning

66 code implementations4 Feb 2016 Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.

Atari Games reinforcement-learning

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