Gradient Estimation Using Stochastic Computation Graphs

  title={Gradient Estimation Using Stochastic Computation Graphs},
  author={John Schulman and Nicolas Heess and Theophane Weber and Pieter Abbeel},
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, which might be part of a probabilistic model or the external world. Estimating the gradient of this loss function, using samples, lies at the core of gradient-based learning algorithms for these problems. We introduce the formalism of stochastic computation graphs—directed acyclic graphs that include both… CONTINUE READING
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