668 citations · 1.3k across the 9 of their papers we have counts for
9 papers
Learning model-based planning from scratch
Razvan Pascanu, Yujia Li, Oriol Vinyals +7
Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model c…
Robust Imitation of Diverse Behaviors
Ziyu Wang, Josh Merel, Scott Reed +3
Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning;…
Distral: Robust Multitask Reinforcement Learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki +5
Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data…
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram +9
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully…
Learning Hierarchical Information Flow with Recurrent Neural Modules
Danijar Hafner, Alex Irpan, James Davidson +1
We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routin…
Particle Value Functions
Chris J. Maddison, Dieterich Lawson, George Tucker +4
The policy gradients of the expected return objective can react slowly to rare rewards. Yet, in some cases agents may wish to emphasize the low or high returns regardless of their…