183 citations · 474 across the 18 of their papers we have counts for
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cs.LG2016★ 150 cited
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…
cs.AI2016★ 183 cited
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…