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Statistical Reinforcement Learning: Modern Machine Learning Approaches > 인공지능

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Statistical Reinforcement Learning: Modern Machine Learning Approaches
판매가격 35,000원
저자 Sugiyama
도서종류 외국도서
출판사 CRC
발행언어 영어
발행일 2015-3
페이지수 206
ISBN 9781439856895
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  • 도서 정보

    도서 상세설명

    Table of 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
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