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5.2 preliminaries 68 In the subsequent diagrams of this chapter, we will use circles to denote stochastic nodes and squares to denote deterministic nodes, as illustrated below. The structure of the graph fully specifies what estimator we will use: SF, PD, or a combination thereof. This graphical notation is shown below, along with the single-variable estimators from Sec- tion 5.2.1. 3. Stochastic nodes, which are distributed conditionally on their parents. Each parent v of a non-input node w is connected to it by a directed edge (v, w). θ Input node Deterministic node Stochastic node 5.2.3 Simple Examples θ x f Gives SF estimator z θ x f Gives PD estimator Several simple examples that illustrate the stochastic computation graph formalism are shown below. The gradient estimators can be described by writing the expectations as integrals and differentiating, as with the simpler estimators from Section 5.2.1. However, they are also implied by the general results that we will present in Section 5.3.PDF Image | OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS
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