Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)

スポンサーリンク

概要

• 論文の詳細を見る

• Direct policy search is a promising reinforcement learning framework in particular for controlling in continuous, high-dimensional systems such as anthropomorphic robot. Policy search often requires a large number of samples for obtaining a stable policy update estimator due to its high flexibility. However, this is prohibitive when the sampling cost is expensive. In this paper, we extend an EM-based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, called Reward-weighted Regression with sample Reuse (R^3), is demonstrated through a toy example.

• 社団法人電子情報通信学会の論文
• 2009-03-04

著者

• Hachiya Hirotaka

  Department Of Computer Science Tokyo Institute Of Technology

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• PETERS Jan

  Max-Planck Institute for Biological Cybernetics Dept. Scholkopf

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

関連論文

• Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Least Absolute Policy Iteration — A Robust Approach to Value Function Approximation
• A New Meta-Criterion for Regularized Subspace Information Criterion
• Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
• A new algorithm of non-Gaussian component analysis with radial kernel functions (Special issue: Information geometry and its applications)
• Methods of cross-domain object matching (情報論的学習理論と機械学習)
• Multi-task learning with least-squares probabilistic classifiers (パターン認識・メディア理解)
• Multi-task learning with least-squares probabilistic classifiers (情報論的学習理論と機械学習)
• Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
• Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)
• Adaptive Ridge Learning in Kernel Eigenspace and Its Model Selection
• On Computational Issues of Semi-Supervised Local Fisher Discriminant Analysis
• Recent Advances and Trends in Large-Scale Kernel Methods
• Syntheses of New Artificial Zinc Finger Proteins Containing Trisbipyridine-ruthenium Amino Acid at The N-or C-terminus as Fluorescent Probes
• Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)
• Constructing Kernel Functions for Binary Regression(Pattern Recognition)
• Optimal design of regularization term and regularization parameter by subspace information criterion
• Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)
• New feature selection method for reinforcement learning: conditional mutual information reveals implicit state-reward dependency (情報論的学習理論と機械学習)
• Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation
• Independent component analysis by direct density-ratio estimation (ニューロコンピューティング)
• A New Meta-Criterion for Regularized Subspace Information Criterion(Pattern Recognition)
• Spectral Methods for Thesaurus Construction
• Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)
• SERAPH: semi-supervised metric learning paradigm with hyper sparsity (情報論的学習理論と機械学習)
• Analysis and improvement of policy gradient estimation (情報論的学習理論と機械学習)
• Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis (情報論的学習理論と機械学習)
• Output divergence criterion for active learning in collaborative settings (数理モデル化と問題解決・バイオ情報学)
• Estimation of squared-loss mutual information from paired and unpaired samples (情報論的学習理論と機械学習)
• Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise (情報論的学習理論と機械学習)
• Canonical dependency analysis based on squared-loss mutual information (情報論的学習理論と機械学習)
• Artist agent A[2]: stroke painterly rendering based on reinforcement learning (パターン認識・メディア理解)
• Artist agent A[2]: stroke painterly rendering based on reinforcement learning (情報論的学習理論と機械学習)
• Generalization Error Estimation for Non-linear Learning Methods(Neural Networks and Bioengineering)
• Improving Precision of the Subspace Information Criterion(Neural Networks and Bioengineering)
• Canonical dependency analysis based on squared-loss mutual information (パターン認識・メディア理解)
• Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (情報論的学習理論と機械学習)
• Modified Newton Approach to Policy Search (情報論的学習理論と機械学習)
• Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
• Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
• Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
• Modified Newton Approach to Policy Search
• Squared-loss Mutual Information Regularization
• Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier
• Feature Selection via l_1-Penalized Squared-Loss Mutual Information
• Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (情報論的学習理論と機械学習)
• Relative Density-Ratio Estimation for Robust Distribution Comparison
• Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering (情報論的学習理論と機械学習)
• Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
• Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
• Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration (情報論的学習理論と機械学習)
• Output Divergence Criterion for Active Learning in Collaborative Settings
• Output Divergence Criterion for Active Learning in Collaborative Settings
• Photochromism of benzylviologens containing methyl groups on pyridinium rings and embedded in solid poly(N-vinyl-2-pyrrolidone) matrix.
• Clustering Unclustered Data : Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
• Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering
• Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
• Squared-loss Mutual Information Regularization
• Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier
• Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering
• Improving Importance Estimation in Pool-based Batch Active Learning for Approximate Linear Regression

もっと見る 閉じる

スポンサーリンク