67 citations · 191 across the 16 of their papers we have counts for
4 papers · 1 filter
On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani +7
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learn…
Learning Compositional Neural Programs for Continuous Control
Thomas Pierrot, Nicolas Perrin, Feryal Behbahani +4
We propose a novel solution to challenging sparse-reward, continuous control problems that require hierarchical planning at multiple levels of abstraction. Our solution, dubbed Alp…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…
Privileged Information Dropout in Reinforcement Learning
Pierre-Alexandre Kamienny, Kai Arulkumaran, Feryal Behbahani +2
Using privileged information during training can improve the sample efficiency and performance of machine learning systems. This paradigm has been applied to reinforcement learning…