2k citations · 7.2k across the 38 of their papers we have counts for
6 papers · 1 filter
The Predictron: End-To-End Learning and Planning
David Silver, Hado van Hasselt, Matteo Hessel +8
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. Th…
Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki +4
Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible t…
Successor Features for Transfer in Reinforcement Learning
André Barreto, Will Dabney, Rémi Munos +4
Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scena…
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez +5
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In th…
Unifying Count-Based Exploration and Intrinsic Motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski +3
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration…
Building Machines That Learn and Think Like People
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…