研究者詳細 - 八谷　大岳

講演・口頭発表等 - 八谷　大岳

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非対称Attentionを用いた集合埋め込みとヘテロジニアス集合マッチング・検索への応用

八谷大岳 [招待有り]

第132回人工知能基本問題研究会(SIG-FPAI) 2025年03月19日

RGBと偏光情報の深層マルチモーダル統合による鏡面検出

吉村優大, 八谷大岳

第27回画像の認識・理解シンポジウム（MIRU2024） 2024年08月

地震ハザード評価のための地震動シミュレーションの代理モデルの構築とその拡張

黒木悠斗, 八谷大岳, 岩城麻子, 前田宜浩, 上田修功, 藤原広行

第27回画像の認識・理解シンポジウム（MIRU2024） 2024年08月

周波数空間MLP-Mixerを用いた鏡面物体検出

黒肥地立樹, 吉村優大, 八谷大岳

第27回画像の認識・理解シンポジウム（MIRU2024） 2024年08月

A deep learning-based approach for forecasting ground motion and precipitation

八谷大岳 [招待有り]

JpGU Meeting 2024 2024年05月24日

ランダムチャンネルパスフィルターを用いたブラックボックスモデルの解釈

二澤大輝, 八谷大岳

情報論的学習理論と機械学習研究会 IBISML2023-42 2024年03月

地下構造データに基づく解釈可能な深層inpaintingによる地震動補間

八谷大岳, 田羅鋤祐果, 岩城麻子, 前田宜浩, 上田修功, 藤原広行

日本地震工学シンポジウム 2023年11月

ニューラルネットを用いた1kmメッシュ気温の推定

北村智文,小林健二,若山郁生,今井崇人,丸山拓海, 上田修功,八谷大岳,高橋温志

気象学会秋季大会 2023年10月

Position-dependent inpainting for ground motion interpolation

Hirotaka Hachiya [招待有り]

Minisymposium, 10th International Congress on Industrial and Applied Mathematics 2023年08月

鏡面フローとハイライトに基づく深層特徴による鏡面検出

吉村優大, 八谷大岳

第26回画像の認識・理解シンポジウム（MIRU2023）, ショートオーラル発表 2023年07月

周波数依存の画像再構成誤差に基づく極小欠陥検出

野村侑平, 八谷大岳

第26回画像の認識・理解シンポジウム（MIRU2023） 2023年07月

強震動データベースの構築と最新技術を用いたデータベースの活用

八谷大岳 [招待有り]

強震動データベースの構築と最新技術を用いたデータベースの活用 2023年02月

Transformer-based fully trainable model for poin;process with virtual sequence vectors;its experimental evaluation

Fumiya Nishizawa, Sujun Hong, Hirotaka Hachiya

第25回情報論的学習理論ワークショップ 2022年11月

Attention-based set embedded vector for set-to-set matching

中村晟人, 八谷大岳

第25回画像の認識・理解シンポジウム（MIRU2022） 2022年07月

Transformer-Based Fully Trainable Model for Point Process with Past Sequence-Representative Vector

Fumiya Nishizawa, Sujun Hong, Hirotaka Hachiya

IBISML2022-1 2022年06月

局所面形状モデルを用いた球面鏡計測

秋吉康平, 八谷大岳

第22回計測自動制御学会システムインテグレーション部門講演会 2021年12月

集合組み込みベクトルを用いたAttentionベースの順不変集合データマッチング

中村晟人, 八谷大岳

第24回情報論的学習理論ワークショップ 2021年11月

Attention-based classification and segmentation for automatic thyroid nodule recognition and diagnosis

戚意強, 鈴木祈史, 八谷大岳, 呉海元

第24回画像の認識・理解シンポジウム（MIRU2021） 2021年07月

全方位画像における正二十面体メッシュを用いた物体検出

髙野誉将, 八谷大岳

第24回画像の認識・理解シンポジウム（MIRU2021） 2021年07月

Encoder-decoder based image transformation approach for integrating precipitation forecasts

Hirotaka Hachiya, Yusuke Masumoto, Yuki Mori, Naonori Ueda

第24回画像の認識・理解シンポジウム（MIRU2021） 2021年07月

Mark-encoded image-based point process

Sujun Hong, Hirotaka Hachiya

第24回画像の認識・理解シンポジウム（MIRU2021） 2021年07月

U-Netを用いた時空間的な降水量ガイダンス統合

八谷大岳, 増本悠介, 森祐貴, 上田修功

日本気象学会2021年度春季大会 2021年05月

マルチクラスAUC最大化を用いた台風発達予報

黒良峻平, 八谷大岳, 嶋田宇大, 上田修功

第23回情報論的学習理論ワークショップ 2020年11月

Deep Inpaintingと空間分布マッチングの組み合わせによる地震動データの空間補完

永吉耕太郎, 八谷大岳, 藤原広行, 上田修功, 岩城麻子, 前田宜浩

第23回情報論的学習理論ワークショップ 2020年11月

オートエンコーダを用いた時系列解析のための高自由度な面的点過程モデル

洪秀俊, 八谷大岳

第23回情報論的学習理論ワークショップ 2020年11月

Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning

Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu

第23回画像の認識・理解シンポジウム（MIRU2020） 2020年08月

Label-CycleGANを用いたドメイン適応のためのCG実写変換

永吉耕太郎, 八谷大岳

第23回画像の認識・理解シンポジウム（MIRU2020） 2020年08月

マルチクロスヒンジ損失を用いた不均衡多クラス分類

黒良峻平, 八谷大岳, 嶋田宇大, 上田修功

情報論的学習理論と機械学習研究会（IBISML） 2020年03月電子情報通信学会

機械学習を用いた南海トラフ巨大地震シミュレータの摩擦パラメータ推定

山本友, 平原和朗,八谷大岳, 上田修功

固体地球科学データ同化に関する研究会 2020年02月

info-cycleGANを用いたドメイン適応のためのCG実写変換

永吉耕太郎, 八谷大岳

第22回情報論的学習理論ワークショップ 2019年11月電子情報通信学会

ニューラルネットワークを用いた急発達台風予報

黒良峻平、八谷大岳、嶋田宇大、上田修功

日本気象学会2019年度秋季大会 2019年10月気象学会

機械学習を用いた異なるパラメータの相対強度マップの統合方法の検討

八谷大岳, 平原和朗, 上田修功

地震学会秋季大会 2019年09月地震学会

機械学習とアンサンブルカルマンフィルタのハイブリッド手法を用いた南海トラフ巨大地震シミュレータの摩擦パラメータ推定

山本友, 平原和朗, 八谷大岳, 高橋温志, 上田修功

地震学会秋季大会 2019年09月地震学会

Triple GANs with adversarial disturbances for discriminative anomaly detection

Hirotaka Hachiya

情報論的学習理論と機械学習研究会（IBISML2019-4） 2019年06月電子情報通信学会

埋込み機械学習による長周期波形からの広帯域地震動合成

岡崎智久、八谷大岳、上田修功、岩城麻子、前田宜浩、藤原広行

日本地球惑星科学連合2019年大会（JpGU2019） 2019年05月

ニューラルネットを用いた逆埋め込み関数の近似と、その文書データ分布の解釈への応用

溝渕湧也, 八谷大岳

ニューロコンピューティング研究会 2019年03月電子情報通信学会

信頼度重み付きクラスタリングによる２次元測距センサの距離推定の頑健化

射手矢和真, 八谷大岳, 中村恭之

第19回計測自動制御学会システムインテグレーション部門講演会 2018年12月計測自動制御学会

2.5D+Orientationアンカーによる物体の距離と向きの推定

佐々木寛史, 八谷大岳

第19回計測自動制御学会システムインテグレーション部門講演会 2018年12月計測自動制御学会

Training Discriminative Model for Anomaly Detection through Generative Adversarial Network

Hirotaka Hachiya

第21回情報論的学習理論ワークショップ（IBIS2018） 2018年11月電子情報通信学会

Laser variational autoencoder for map construction and self-localization

Shohei Wakita, Takayuki Nakamura, Hirotaka Hachiya

IEEE International Conference on Systems, Man, and Cybernetics 2018年10月 IEEE

機械学習を用いた広帯域地震動合成の試み

岡﨑智久、八谷大岳、前田宜浩、岩城麻子、藤原広行、上田修功

地震学会2018年度秋季大会 2018年10月地震学会

オートエンコーダを用いた環境地図の特徴表現と自己位置推定

脇田翔平、中村恭之、八谷大岳

ロボティクス・メカトロニクス講演会 2018年06月日本機械学会ロボティクス・メカトロニクス部門

自由領域制限による経路教示と経路計画のハイブリッド自律走行

野村雅也、中村恭之、八谷大岳

第２３回ロボティクスシンポジア 2018年03月計測自動制御学会、日本ロボット学会、日本機械学会

3Dアンカーによる距離推定とロボットナビゲーションへの応用

八谷大岳、斎藤侑輝、射手矢和真、野村雅也、中村恭之

第２３回ロボティクスシンポジア 2018年03月計測自動制御学会、日本ロボット学会、日本機械学会

透視投影アンカーを用いた特定物体の検出および距離推定

八谷大岳、斎藤侑輝、射手矢和真、中村恭之

第１８回システムインテグレーション部門講演会 2017年12月計測自動制御学会

経路教示と経路計画のハイブリッド自律走行

八谷大岳、野村雅也、脇田翔平、射手矢和真、中村恭之

つくばチャレンジ2017参加レポート集 2017年

ディープラーニングによる特定人物検出と距離推定

八谷大岳、野村雅也、脇田翔平、射手矢和真、中村恭之

つくばチャレンジ2017参加レポート集 2017年

NSH: Normality Sensitive Hashing for Anomaly Detection

Hirotaka Hachiya, Masakazu Matsugu

2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) 2013年 IEEE

Locality sensitive hashing (LSH) is a computationally efficient alternative to the distance based anomaly detection. The main advantages of LSH lie in constant detection time, low memory requirement, and simple implementation. However, since the metric of distance in LSHs does not consider the property of normal training data, a naive use of existing LSHs would not perform well. In this paper, we propose a new hashing scheme so that hash functions are selected dependently on the properties of the normal training data for reliable anomaly detection. The distance metric of the proposed method, called NSH (Normality Sensitive Hashing) is theoretically interpreted in terms of the region of normal training data and its effectiveness is demonstrated through experiments on real-world data. Our results are favorably comparable to state-of-the arts with the low-level features.

Feature Selection via l₁-Penalized Squared-Loss Mutual Information (情報論的学習理論と機械学習)

JITKRITTUM Wittawat, HACHIYA Hirotaka, SUGIYAMA Masashi

電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning 2012年03月05日一般社団法人電子情報通信学会

Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose l_1-LSMI, an l_1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that l_1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.

Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration (情報論的学習理論と機械学習)

ZHAO Tingting, HACHIYA Hirotaka, SUGIYAMA Masashi

The policy gradient approach is a flexible and powerful reinforcement learning method particularly for problems with continuous actions such as robot control. A common challenge in this scenario is how to stabilize policy gradient estimates for reliable policy updates. In this paper, we combine the following three ideas and give a highly stable and practical policy gradient method: (a) the policy gradients with parameter based exploration, which is a recently proposed policy search method with high stability, (b) an importance sampling technique, which allows us to reuse previously gathered data in an unbiased way, and (c) an optimal baseline, which minimizes the variance of gradient estimates with their unbiasedness being maintained. For the proposed method, we give theoretical analysis of the variance of gradient estimates and show its usefulness through experiments.

Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)

NAM Hyunha, HACHIYA Hirotaka, SUGIYAMA Masashi

電子情報通信学会技術研究報告 : 信学技報 2011年11月09日一般社団法人電子情報通信学会

Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as audio tagging, image annotation, video search, and text mining. In such a multi-label scenario, taking into account correlation between multiple labels can boost the classification accuracy. However, this in turn makes classifier training more challenging because handling multiple labels tends to induce a high-dimensional optimization problem. In this paper, we propose a highly scalable multi-label classifier based on a computationally efficient classification algorithm called the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.

Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)

YAMADA Makoto, SUZUKI Taiji, KANAMORI Takafumi, HACHIYA Hirotaka, SUGIYAMA Masashi

電子情報通信学会技術研究報告 : 信学技報 2011年11月09日一般社団法人電子情報通信学会

Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity test. However, since density-ratio functions often possess high fluctuation, divergence estimation is still a challenging task in practice. In this paper, we propose to use relative divergences for distribution comparison, which involves approximation of relative density-ratios. Since relative density-ratios are always smoother than corresponding ordinary density-ratios, our proposed method is favorable in terms of the non-parametric convergence speed. Furthermore, we show that the proposed divergence estimator has asymptotic variance independent of the model complexity under a parametric setup, implying that the proposed estimator hardly overfits even with complex models. Through experiments, we demonstrate the usefulness of the proposed approach.

Modified Newton Approach to Policy Search (情報論的学習理論と機械学習)

HACHIYA Hirotaka, MORIMURA Tetsuro, MAKINO Takaki, SUGIYAMA Masashi

電子情報通信学会技術研究報告 : 信学技報 2011年11月09日一般社団法人電子情報通信学会

The natural policy gradient method was shown to be a useful approach to policy search in reinforcement learning. However, its potential weakness is that information on returns is not reflected in the metric of natural gradients, implying that it is not adaptive to data and thus less flexible. To overcome this, we propose to use Newton's method which uses the Hessian of the expected return as a metric. However, the naive implementation of Newton's method does not guarantee the Hessian to be negative definite, which causes instability on policy updates. To cope with this problem, we propose an adaptive scheme to keep the Hessian nonnegative. We demonstrate the effectiveness of our proposed method in standard reinforcement learning tasks.

Artist agent A[2]: stroke painterly rendering based on reinforcement learning (パターン認識・メディア理解)

Xie Ning, Hachiya Hirotaka, Sugiyama Masashi

電子情報通信学会技術研究報告. PRMU, パターン認識・メディア理解 2011年08月29日一般社団法人電子情報通信学会

Artist agent A[2]: stroke painterly rendering based on reinforcement learning (情報論的学習理論と機械学習)

Xie Ning, Hachiya Hirotaka, Sugiyama Masashi

電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning 2011年08月29日一般社団法人電子情報通信学会

Artist Agent A2: Stroke Painterly Rendering Based on Reinforcement Learning

Ning Xie, Hirotaka Hachiya, Masashi Sugiyama

研究報告コンピュータビジョンとイメージメディア（CVIM） 2011年08月29日

Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. The major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement-learning (RL) agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of state space, action space, and a reward function tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. The major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement-learning (RL) agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of state space, action space, and a reward function tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.

Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)

Sugiyama Masashi, Yamada Makoto, Kimura Manabu, HACHIYA Hirotaka

電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning 2011年03月21日一般社団法人電子情報通信学会

A recently-proposed information-maximization clustering method (Gomes et al., NIPS2010) learns a kernel logistic regression classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of a logistic model, which is substantially easier than discrete optimization of cluster assignments. However, this method still suffers from two weaknesses: (i) manual tuning of kernel parameters is necessary, and (ii) finding a good local optimal solution is not straightforward due to the strong non-convexity of logistic-regression learning. In this paper, we first show that the kernel parameters can be systematically optimized by maximizing mutual information estimates. We then propose an alternative information-maximization clustering approach using a squared-loss variant of mutual information. This novel approach allows us to obtain clustering solutions analytically in a computationally very efficient way. Through experiments, we demonstrate the usefulness of the proposed approaches.

動的計画法によるリターン分布推定

森村哲郎, 杉山将, 鹿島久嗣, 八谷大岳, 田中利幸

電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning 2010年10月28日一般社団法人電子情報通信学会

標準的な強化学習の枠組みでは, Bellman方程式を用いてリターン(割引報酬和)の期待値を推定し,意思決定を行う.近年,我々はこの枠組みを拡張し,分布Bellman方程式を用いてリターンの分布を推定する方法を提案した.これにより,バリュー・アット・リスクなどのリスクを考慮した任意の指標に基づく意思決定が行えるようになったが,分布Bellman方程式を用いた手法の収束性などの理論的性質は末だ解明されていない.本論文では,分布Bellman方程式を動的計画法によって解いた場合,解が初期近似分布に依存せず常に真のリターン分布に収束することを証明する.さらに,リターン分布推定量のモーメントの収束率も示す.最後に,得られた理論結果から,既存のリターン分布推定法の改善方法を提案し,数値実験からその有効性を示す.

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)

Hachiya Hirotaka, Sugiyama Masashi

電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning 2010年06月07日一般社団法人電子情報通信学会

Model-free reinforcement learning (RL) is a machine learning approach to decision making in unknown environment. However, real-world RL tasks often involve high-dimensional state space, and then standard RL methods do not perform well. In this paper, we propose a new feature selection framework for coping with high dimensionality. Our proposed framework adopts conditional mutual information between state and return sequences as a feature selection criterion, allowing the evaluation of implicit state-reward dependency. The conditional mutual information is approximated by a least-squares method, which results in a computationally efficient feature selection procedure. The usefulness of the proposed method is demonstrated on simulated mobile-robot navigation experiments.

Improving Model-based Reinforcement Learning with Multitask Learning (数理モデル化と問題解決(MPS) Vol.2009-MPS-76)

SIMM JAAK, SUGIYAMA MASASHI, HACHIYA HIROTAKA

情報処理学会研究報告 2010年02月情報処理学会

Improving Model-based Reinforcement Learning with Multitask Learning (バイオ情報学(BIO) Vol.2009-BIO-19)

Jaak Simm, Masashi Sugiyama, Hirotaka Hachiya

研究報告バイオ情報学（BIO） 2009年12月10日情報処理学会

We introduce an extension to standard reinforcement learning setting called observational RL (ORL) where additional observational information is available to the agent. This allows the agent to learn the system dynamics with fewer data samples, which is an essential feature for practical applications of RL methods. We show that ORL can be formulated as a multitask learning problem. A similarity-based and a component-based multitask learning methods are proposed for learning the transition probabilities of the ORL problem. The effectiveness of the proposed methods is evaluated in experiments of grid world and object lifting tasks.We introduce an extension to standard reinforcement learning setting called observational RL (ORL) where additional observational information is available to the agent. This allows the agent to learn the system dynamics with fewer data samples, which is an essential feature for practical applications of RL methods. We show that ORL can be formulated as a multitask learning problem. A similarity-based and a component-based multitask learning methods are proposed for learning the transition probabilities of the ORL problem. The effectiveness of the proposed methods is evaluated in experiments of grid world and object lifting tasks.

Conditional Density Estimation Based on Density Ratio Estimation

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara

研究報告バイオ情報学（BIO） 2009年12月10日

Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation.Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation.

Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)

Akiyama Takayuki, Hachiya Hirotaka, Sugiyama Masashi

電子情報通信学会技術研究報告. NC, ニューロコンピューティング 2009年03月04日一般社団法人電子情報通信学会

Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. The proposed method combined with LSPI is called active policy iteration (API). Through simulations we demonstrate the usefulness of API.

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

Hachiya Hirotaka, Peters Jan, Sugiyama Masashi

電子情報通信学会技術研究報告. NC, ニューロコンピューティング 2009年03月04日一般社団法人電子情報通信学会

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.

Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)

Hachiya Hirotaka, Akiyama Takayuki, Sugiyama Masashi

電子情報通信学会技術研究報告. NC, ニューロコンピューティング 2007年12月15日一般社団法人電子情報通信学会

Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past. A common approach is to use importance sampling techniques for compensating for the bias caused by the difference between data-collecting policies and the target policy. However, existing off-policy methods do not often take the variance of value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a statistical machine learning theory.

Robot Control by Least-Squares Policy Iteration with Geodesic Gaussian Kernels

Hachiya Hirotaka, Sugiyama Masashi

人工知能学会全国大会論文集 2007年人工知能学会

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