1.1k citations · 4.1k across the 38 of their papers we have counts for
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Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap +1
Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic application…
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +2
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their…
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Jack W Rae, Jonathan J Hunt, Tim Harley +5
Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks. These models appear promising for applications such as language mod…
Learning and Transfer of Modulated Locomotor Controllers
Nicolas Heess, Greg Wayne, Yuval Tassa +3
We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor pr…
Towards deep learning with segregated dendrites
Jordan Guergiuev, Timothy P. Lillicrap, Blake A. Richards
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning…