Reinforcement Learning in POMDPs with Function Approximation. - Hajime Kimura; Kazuteru Miyazaki; Shigenobu Kobayashi (ICML, 1997)

k-Certainty Exploration Method: An Action Selector to Identify the Environment in Reinforcement Learning. - Kazuteru Miyazaki; Masayuki Yamamura; Shigenobu Kobayashi (Artif. Intell., 1997)

Multi-agent Reinforcement Learning for Crane Control Problem: Designing Rewards for Conflict Resolution. - Sachiyo Arai; Kazuteru Miyazaki; Shigenobu Kobayashi (ISADS, 1999)

Reinforcement learning for penalty avoiding policy making. - Kazuteru Miyazaki; Shigenobu Kobayashi (SMC, 2000)

Rationality of Reward Sharing in Multi-agent Reinforcement Learning. - Kazuteru Miyazaki; Shigenobu Kobayashi (New Gener. Comput., 2001)

Development of a reinforcement learning system to play Othello. - Kazuteru Miyazaki; Sougo Tsuboi; Shigenobu Kobayashi (Artif. Life Robotics, 2004)

Multi User Learning Agent on the Distribution of MDPs. - Daisuke Katagami; Katsumi Nitta; Kazuteru Miyazaki (RO-MAN, 2006)

Reinforcement Learning for Penalty Avoidance in Continuous State Spaces. - Kazuteru Miyazaki; Shigenobu Kobayashi (J. Adv. Comput. Intell. Intell. Informatics, 2007)

Proposal of Exploitation-Oriented Learning PS-r#. - Kazuteru Miyazaki; Shigenobu Kobayashi (IDEAL, 2008)

Exploitation-Oriented Learning PS-r#. - Kazuteru Miyazaki; Shigenobu Kobayashi (J. Adv. Comput. Intell. Intell. Informatics, 2009)

A New Improved Penalty Avoiding Rational Policy Making Algorithm for Keepaway with Continuous State Spaces. - Takuji Watanabe; Kazuteru Miyazaki; Hiroaki Kobayashi (J. Adv. Comput. Intell. Intell. Informatics, 2009)

The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces. - Kazuteru Miyazaki (IDEAL, 2010)

Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajectory Generation of Biped Robot. - Seiya Kuroda; Kazuteru Miyazaki; Hiroaki Kobayashi (EWRL, 2011)

Proposal and Evaluation of the Active Course Classification Support System with Exploitation-Oriented Learning. - Kazuteru Miyazaki; Masaaki Ida (EWRL, 2011)

Evaluation of the Improved Penalty Avoiding Rational Policy Making Algorithm in Real World Environment. - Kazuteru Miyazaki; Masaki Itou; Hiroaki Kobayashi (ACIIDS (1), 2012)

Proposal of an Active Course Classification Support system with Exploitation-oriented Learning extended by positive and negative examples. - Kazuteru Miyazaki; Masaaki Ida (SCIS&ISIS, 2012)

Introduction of Fixed Mode States into Online Reinforcement Learning with Penalties and Rewards and its Application to Biped Robot Waist Trajectory Generation. - Seiya Kuroda; Kazuteru Miyazaki; Hiroaki Kobayashi (J. Adv. Comput. Intell. Intell. Informatics, 2012)

Proposal of the Continuous-Valued Penalty Avoiding Rational Policy Making Algorithm. - Kazuteru Miyazaki (J. Adv. Comput. Intell. Intell. Informatics, 2012)

Proposal of an Exploitation-oriented Learning Method on Multiple Rewards and Penalties Environments and the Design Guideline. - Kazuteru Miyazaki (J. Comput., 2013)

The Necessity of a Secondary System in Machine Consciousness. - Kazuteru Miyazaki; Jun'ichi Takeno (BICA, 2014)

A Study of an Indirect Reward on Multi-agent Environments. - Kazuteru Miyazaki (BICA, 2016)

Proposal of an Action Selection Strategy with Expected Failure Probability and Its Evaluation in Multi-agent Reinforcement Learning. - Kazuteru Miyazaki; Koudai Furukawa; Hiroaki Kobayashi (EUMAS/AT, 2016)

Proposal and Evaluation of an Action Selection Strategy with Expected Failure Probability in Multi-agent Learning. - Kazuteru Miyazaki; Koudai Furukawa; Hiroaki Kobayashi (ICA, 2016)

Proposal of a Deep Q-network with Profit Sharing. - Kazuteru Miyazaki (BICA, 2017)

Exploitation-Oriented Learning with Deep Learning - Introducing Profit Sharing to a Deep Q-Network -. - Kazuteru Miyazaki (J. Adv. Comput. Intell. Intell. Informatics, 2017)

Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning. - Kazuteru Miyazaki; Koudai Furukawa; Hiroaki Kobayashi (J. Adv. Comput. Intell. Intell. Informatics, 2017)

A Proposal for Reducing the Number of Trial-and-Error Searches for Deep Q-Networks Combined with Exploitation-Oriented Learning. - Naoki Kodama; Kazuteru Miyazaki; Taku Harada (ICMLA, 2018)

Proposal and Evaluation of an Indirect Reward Assignment Method for Reinforcement Learning by Profit Sharing Method. - Kazuteru Miyazaki; Naoki Kodama; Hiroaki Kobayashi (IntelliSys (1), 2018)

Proposal of Detour Path Suppression Method in PS Reinforcement Learning and Its Application to Altruistic Multi-agent Environment. - Daisuke Shiraishi; Kazuteru Miyazaki; Hiroaki Kobayashi (PRIMA, 2018)

Consistency Assessment between Diploma Policy and Curriculum Policy using Character-Level CNN. - Kazuteru Miyazaki; Masaaki Ida (SCIS&ISIS, 2018)

Deep Reinforcement Learning with Dual Targeting Algorithm. - Naoki Kodama; Taku Harada; Kazuteru Miyazaki (IJCNN, 2019)

Application of Deep Reinforcement Learning to Decision-Making System based on Consciousness. - Kazuteru Miyazaki (BICA*AI, 2020)

Classification of Medical Data using Character-level CNN. - Kazuteru Miyazaki (ICISS, 2020)

Home Energy Management Algorithm Based on Deep Reinforcement Learning Using Multistep Prediction. - Naoki Kodama; Taku Harada; Kazuteru Miyazaki (IEEE Access, 2021)

Proposal and evaluation of deep exploitation-oriented learning under multiple reward environment. - Kazuteru Miyazaki (Cogn. Syst. Res., 2021)

Modeling of placebo effect in stochastic reward tasks by reinforcement learning. - Kazuteru Miyazaki (BICA*AI, 2022)

Traffic Signal Control System Using Deep Reinforcement Learning With Emphasis on Reinforcing Successful Experiences. - Naoki Kodama; Taku Harada; Kazuteru Miyazaki (IEEE Access, 2022)

Suppression of negative tweets using reinforcement learning systems. - Kazuteru Miyazaki; Hitomi Miyazaki (Cogn. Syst. Res., 2024)

Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning. - Kazuteru Miyazaki (J. Robotics Mechatronics, 2024)

Editorial: Cutting Edge of Reinforcement Learning and its Hybrid Methods. - Kazuteru Miyazaki; Keiki Takadama (J. Adv. Comput. Intell. Intell. Informatics, 2024)

Proposal of a Course-Classification Support System Using Deep Learning and its Evaluation When Combined with Reinforcement Learning. - Kazuteru Miyazaki; Shu Yamaguchi; Rie Mori; Yumiko Yoshikawa; Takanori Saito; Toshiya Suzuki (J. Adv. Comput. Intell. Intell. Informatics, 2024)

Performance evaluation of character-level CNNs using tweet data and analysis for weight perturbations. - Kazuteru Miyazaki; Masaaki Ida (Artif. Life Robotics, 2024)