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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5.10.2 Policy Gradients in Reinforcement Learning. 81 6 conclusion 84 6.1 Frontiers 85 LIST OF FIGURES Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Illustration of single-path and vine procedures 2D robot models used for TRPO locomotion experiments 30 Neural networks used for TRPO experiments 30 Learning curves for TRPO locomotion tasks 32 Computation of factored discrete probability distribution in Atari domain 43 Learning curves for TRPO atari experiments 44 3D robot models used in GAE experiments 56 Learning curves for GAE experiments on cart-pole system 58 Learning curves for GAE experiments on 3D locomotion 59 Learning curves and stills from 3D standing 60 Simple stochastic computation graphs 69 Deterministic computation graphs of surrogate functions for gra- dient estimation 73 Stochastic computation graphs for NVIL and VAE models 82 Stochastic Computation Graphs for MDPs and POMDPs 83 LIST OF TABLES 26 Table 1 Performance comparison for vision-based RL algorithms on the Atari domain 33 5

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