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3.13 Approximating Factored Policies with Neural Networks 42 3.14 Experiment Parameters 43 3.15 Learning Curves for the Atari Domain 44 4 generalized advantage estimation 4.1 Overview 45 4.2 Preliminaries 46 4.3 Advantage function estimation 4.4 Interpretation as Reward Shaping 4.5 Value Function Estimation 53 4.6 Experiments 54 45 4.6.1 Policy Optimization Algorithm 55 4.6.2 Experimental Setup 56 4.6.3 Experimental Results 57 4.7 Discussion 59 4.8 Frequently Asked Questions 61 4.8.1 What’s the Relationship with Compatible Features? 4.8.2 Why Don’t You Just Use a Q-Function? 62 4.9 Proofs 62 5 stochastic computation graphs 64 5.1 Overview 64 5.2 Preliminaries 65 5.2.1 Gradient Estimators for a Single Random Variable 5.2.2 Stochastic Computation Graphs 67 5.2.3 Simple Examples 68 61 65 5.3 Main Results on Stochastic Computation Graphs 5.3.1 Gradient Estimators 70 5.3.2 Surrogate Loss Functions 72 5.3.3 Higher-Order Derivatives. 73 5.4 Variance Reduction 73 5.5 Algorithms 74 5.6 Related Work 74 5.7 Conclusion 76 5.8 Proofs 77 5.9 Surrogate as an Upper Bound, and MM Algorithms 5.10 Examples 79 70 5.10.1 Generalized EM Algorithm and Variational Inference. 79 49 51 78 contents 4PDF Image | OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS
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