Statistical reinforcement learning : modern machine learning approaches /: modern machine learning approaches. (2015)

• Record Type: Book
• Further Information: Note: By Masashi Sugiyama, Hirotaka Hachiya, Tetsuro Morimura.
• Authors: Sugiyama, Masashi, 1974-; Hachiya, Hirotaka; Morimura, Tetsuro
• Contents: Introduction to Reinforcement Learning; Reinforcement Learning; Mathematical Formulation; Structure of the Book; Model-Free Policy Iteration; Model-Free Policy Search; Model-Based Reinforcement Learning; ; MODEL-FREE POLICY ITERATION; ; Policy Iteration with Value Function Approximation; Value Functions; State Value Functions; State-Action Value Functions; Least-Squares Policy Iteration; Immediate-Reward Regression; Algorithm; Regularization; Model Selection; Remarks; ; Basis Design for Value Function Approximation ; Gaussian Kernels on Graphs; MDP-Induced Graph; Ordinary Gaussian Kernels; Geodesic Gaussian Kernels; Extension to Continuous State Spaces; Illustration; Setup; Geodesic Gaussian Kernels; Ordinary Gaussian Kernels; Graph-Laplacian Eigenbases; Diffusion Wavelets; Numerical Examples; Robot-Arm Control; Robot-Agent Navigation; Remarks; ; Sample Reuse in Policy Iteration ; Formulation; Off-Policy Value Function Approximation; Episodic Importance Weighting; Per-Decision Importance Weighting; Adaptive Per-Decision Importance Weighting; Illustration; Automatic Selection of Flattening Parameter; Importance-Weighted Cross-Validation; Illustration; Sample-Reuse Policy Iteration; Algorithm; Illustration; Numerical Examples; Inverted Pendulum; Mountain Car; Remarks; ; Active Learning in Policy Iteration ; Efficient Exploration with Active Learning; Problem Setup; Decomposition of Generalization Error; Estimation of Generalization Error; Designing Sampling Policies; Illustration; Active Policy Iteration; Sample-Reuse Policy Iteration with Active Learning; Illustration; Numerical Examples; Remarks; ; Robust Policy Iteration ; Robustness and Reliability in Policy Iteration; Robustness; Reliability; Least Absolute Policy Iteration; Algorithm; Illustration; Properties; Numerical Examples; Possible Extensions; Huber Loss; Pinball Loss; Deadzone-Linear Loss; Chebyshev Approximation; Conditional Value-At-Risk; Remarks; ; MODEL-FREE POLICY SEARCH; ; Direct Policy Search by Gradient Ascent ; Formulation; Gradient Approach; Gradient Ascent; Baseline Subtraction for Variance Reduction; Variance Analysis of Gradient Estimators; Natural Gradient Approach Natural Gradient Ascent; Illustration; Application in Computer Graphics: Artist Agent; Sumie Paining Design of States, Actions, and Immediate Rewards; Experimental Results; Remarks; ; Direct Policy Search by Expectation-Maximization ; Expectation-Maximization Approach; Sample Reuse; Episodic Importance Weighting; Per-Decision Importance Weight; Adaptive Per-Decision Importance Weighting; Automatic Selection of Flattening Parameter; Reward-Weighted Regression with Sample Reuse; Numerical Examples; Remarks; ; Policy-Prior Search ; Formulation; Policy Gradients with Parameter-Based Exploration Policy-Prior Gradient Ascent; Baseline Subtraction for Variance Reduction; Variance Analysis of Gradient Estimators; Numerical Examples; Sample Reuse in Policy-Prior Search Importance Weighting; Variance Reduction by Baseline Subtraction; Numerical Examples; Remarks; ; MODEL-BASED REINFORCEMENT LEARNING; ; Transition Model Estimation ; Conditional Density Estimation; Regression-Based Approach; Q-Neighbor Kernel Density Estimation; Least-Squares Conditional Density Estimation; Model-Based Reinforcement Learning; Numerical Examples; Continuous Chain Walk; Humanoid Robot Control; Remarks; ; Dimensionality Reduction for Transition Model Estimation ; Sufficient Dimensionality Reduction; Squared-Loss Conditional Entropy; Conditional Independence; Dimensionality Reduction with SCE; Relation to Squared-Loss Mutual Information; Numerical Examples; Artificial and Benchmark Datasets Humanoid Robot; Remarks; ; References; Index
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations
• Subjects: 006.31; Reinforcement learning
• Languages: English
• ISBNs: 9781439856901
• Notes: Note: Description based on CIP data; resource not viewed.
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