265 citations · 1.6k across the 43 of their papers we have counts for
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cs.AI2015★ 265 cited
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
cs.LG2015★ 173 cited
Gradient Estimation Using Stochastic Computation Graphs
John Schulman, Nicolas Heess, Theophane Weber +1
In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables,…