OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS

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OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS ( optimizing-expectations-from-deep-reinforcement-learning-to- )

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Algorithm 4 Compute Gradient Estimator for Stochastic Computation Graph for v ∈ Graph do  ◃ Initialization at output nodes ◃ Reverse traversal gv = 0dim v end for 1dim v if v ∈ C otherwise Compute Qˆ w for all nodes w ∈ Graph for v in ReverseTopologicalSort(NonInputs) do for w ∈ parentsv do if not IsStochastic(w) then if IsStochastic(v) then ˆ gw += ( ∂ log p(v | parentsv))Qw else gw += ( ∂v )T gv end if end if end for end for return [gθ]θ∈Θ ∂w ∂w 5.6 related work 75

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