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CONTENTS 1 introduction 1 1.1 Reinforcement Learning 1 1.2 Deep Learning 1 1.3 Deep Reinforcement Learning 2 1.4 What to Learn, What to Approximate 3 1.5 Optimizing Stochastic Policies 1.6 Contributions of This Thesis 2 background 8 5 6 2.1 Markov Decision Processes 2.2 The Episodic Reinforcement Learning Problem 8 2.3 Partially Observed Problems 9 2.4 Policies 10 2.5 Deriviative Free Optimization of Policies 11 2.6 Policy Gradients 12 3 trust region policy optimization 18 3.1 Overview 18 3.2 Preliminaries 19 3.3 Monotonic Improvement Guarantee for General Stochastic Policies 3.4 Optimization of Parameterized Policies 23 3.5 Sample-Based Estimation of the Objective and Constraint 24 3.5.1 Single Path 25 3.5.2 Vine 25 3.6 Practical Algorithm 27 3.7 Connections with Prior Work 28 3.8 Experiments 29 3.8.1 Simulated Robotic Locomotion 30 32 34 3.12.1 Computing the Fisher-Vector Product 40 3 8 3.8.2 Playing Games from Images 3.9 Discussion 33 3.10 Proof of Policy Improvement Bound 3.11 Perturbation Theory Proof of Policy Improvement Bound 37 3.12 EfficientlySolvingtheTrust-RegionConstrainedOptimizationProblem 39 21PDF Image | OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS
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