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ORCIDKazuteru Miyazaki
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2020 – today
- 2024
[j20]Kazuteru Miyazaki, Masaaki Ida:
Performance evaluation of character-level CNNs using tweet data and analysis for weight perturbations. Artif. Life Robotics 29(2): 266-273 (2024)- [j19]Kazuteru Miyazaki
, Hitomi Miyazaki:
Suppression of negative tweets using reinforcement learning systems. Cogn. Syst. Res. 84: 101207 (2024) - [j18]Kazuteru Miyazaki, Keiki Takadama:
Editorial: Cutting Edge of Reinforcement Learning and its Hybrid Methods. J. Adv. Comput. Intell. Intell. Informatics 28(2): 379 (2024) - [j17]Kazuteru Miyazaki, Shu Yamaguchi, Rie Mori, Yumiko Yoshikawa, Takanori Saito, Toshiya Suzuki:
Proposal of a Course-Classification Support System Using Deep Learning and its Evaluation When Combined with Reinforcement Learning. J. Adv. Comput. Intell. Intell. Informatics 28(2): 454-467 (2024) - [j16]Kazuteru Miyazaki:
Enhanced Naive Agent in Angry Birds AI Competition via Exploitation-Oriented Learning. J. Robotics Mechatronics 36(3): 580-588 (2024) - 2022
- [j15]Naoki Kodama, Taku Harada, Kazuteru Miyazaki:
Traffic Signal Control System Using Deep Reinforcement Learning With Emphasis on Reinforcing Successful Experiences. IEEE Access 10: 128943-128950 (2022) - [c24]Kazuteru Miyazaki:
Modeling of placebo effect in stochastic reward tasks by reinforcement learning. BICA*AI 2022: 255-262 - 2021
- [j14]Naoki Kodama, Taku Harada, Kazuteru Miyazaki:
Home Energy Management Algorithm Based on Deep Reinforcement Learning Using Multistep Prediction. IEEE Access 9: 153108-153115 (2021) - [j13]Kazuteru Miyazaki:
Proposal and evaluation of deep exploitation-oriented learning under multiple reward environment. Cogn. Syst. Res. 70: 29-39 (2021) - 2020
- [c23]Kazuteru Miyazaki:
Application of Deep Reinforcement Learning to Decision-Making System based on Consciousness. BICA*AI 2020: 631-636 - [c22]Kazuteru Miyazaki:
Classification of Medical Data using Character-level CNN. ICISS 2020: 43-47
2010 – 2019
- 2019
- [c21]Naoki Kodama, Taku Harada, Kazuteru Miyazaki:
Deep Reinforcement Learning with Dual Targeting Algorithm. IJCNN 2019: 1-6 - 2018
- [c20]Naoki Kodama, Kazuteru Miyazaki, Taku Harada:
A Proposal for Reducing the Number of Trial-and-Error Searches for Deep Q-Networks Combined with Exploitation-Oriented Learning. ICMLA 2018: 983-988 - [c19]Kazuteru Miyazaki, Naoki Kodama, Hiroaki Kobayashi:
Proposal and Evaluation of an Indirect Reward Assignment Method for Reinforcement Learning by Profit Sharing Method. IntelliSys (1) 2018: 187-200 - [c18]Daisuke Shiraishi, Kazuteru Miyazaki, Hiroaki Kobayashi:
Proposal of Detour Path Suppression Method in PS Reinforcement Learning and Its Application to Altruistic Multi-agent Environment. PRIMA 2018: 638-645 - [c17]Kazuteru Miyazaki, Masaaki Ida:
Consistency Assessment between Diploma Policy and Curriculum Policy using Character-Level CNN. SCIS&ISIS 2018: 626-631 - 2017
- [j12]Keiki Takadama, Kazuteru Miyazaki:
Editorial: Cutting Edge of Reinforcement Learning and its Hybrid Methods. J. Adv. Comput. Intell. Intell. Informatics 21(5): 833 (2017) - [j11]Kazuteru Miyazaki:
Exploitation-Oriented Learning with Deep Learning - Introducing Profit Sharing to a Deep Q-Network -. J. Adv. Comput. Intell. Intell. Informatics 21(5): 849-855 (2017) - [j10]Kazuteru Miyazaki, Koudai Furukawa, Hiroaki Kobayashi:
Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning. J. Adv. Comput. Intell. Intell. Informatics 21(5): 930-938 (2017) - [c16]Kazuteru Miyazaki:
Proposal of a Deep Q-network with Profit Sharing. BICA 2017: 302-307 - 2016
- [c15]Kazuteru Miyazaki:
A Study of an Indirect Reward on Multi-agent Environments. BICA 2016: 94-101 - [c14]Kazuteru Miyazaki, Koudai Furukawa, Hiroaki Kobayashi:
Proposal of an Action Selection Strategy with Expected Failure Probability and Its Evaluation in Multi-agent Reinforcement Learning. EUMAS/AT 2016: 172-186 - [c13]Kazuteru Miyazaki, Koudai Furukawa, Hiroaki Kobayashi:
Proposal and Evaluation of an Action Selection Strategy with Expected Failure Probability in Multi-agent Learning. ICA 2016: 127-130 - 2014
- [c12]Kazuteru Miyazaki, Jun'ichi Takeno:
The Necessity of a Secondary System in Machine Consciousness. BICA 2014: 15-22 - 2013
- [j9]Kazuteru Miyazaki:
Proposal of an Exploitation-oriented Learning Method on Multiple Rewards and Penalties Environments and the Design Guideline. J. Comput. 8(7): 1683-1690 (2013) - 2012
- [j8]Kazuteru Miyazaki:
Proposal of the Continuous-Valued Penalty Avoiding Rational Policy Making Algorithm. J. Adv. Comput. Intell. Intell. Informatics 16(2): 183-190 (2012) - [j7]Seiya Kuroda, Kazuteru Miyazaki, Hiroaki Kobayashi:
Introduction of Fixed Mode States into Online Reinforcement Learning with Penalties and Rewards and its Application to Biped Robot Waist Trajectory Generation. J. Adv. Comput. Intell. Intell. Informatics 16(6): 758-768 (2012) - [c11]Kazuteru Miyazaki, Masaki Itou, Hiroaki Kobayashi:
Evaluation of the Improved Penalty Avoiding Rational Policy Making Algorithm in Real World Environment. ACIIDS (1) 2012: 270-280 - [c10]Kazuteru Miyazaki, Masaaki Ida:
Proposal of an Active Course Classification Support system with Exploitation-oriented Learning extended by positive and negative examples. SCIS&ISIS 2012: 1520-1527 - 2011
- [c9]Seiya Kuroda, Kazuteru Miyazaki, Hiroaki Kobayashi:
Introduction of Fixed Mode States into Online Profit Sharing and Its Application to Waist Trajectory Generation of Biped Robot. EWRL 2011: 297-308 - [c8]Kazuteru Miyazaki, Masaaki Ida:
Proposal and Evaluation of the Active Course Classification Support System with Exploitation-Oriented Learning. EWRL 2011: 333-344 - 2010
- [c7]Kazuteru Miyazaki:
The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces. IDEAL 2010: 178-185
2000 – 2009
- 2009
- [j6]Kazuteru Miyazaki, Shigenobu Kobayashi:
Exploitation-Oriented Learning PS-r#. J. Adv. Comput. Intell. Intell. Informatics 13(6): 624-630 (2009) - [j5]Takuji Watanabe, Kazuteru Miyazaki, Hiroaki Kobayashi:
A New Improved Penalty Avoiding Rational Policy Making Algorithm for Keepaway with Continuous State Spaces. J. Adv. Comput. Intell. Intell. Informatics 13(6): 675-682 (2009) - 2008
- [c6]Kazuteru Miyazaki, Shigenobu Kobayashi:
Proposal of Exploitation-Oriented Learning PS-r#. IDEAL 2008: 1-8 - 2007
- [j4]Kazuteru Miyazaki, Shigenobu Kobayashi:
Reinforcement Learning for Penalty Avoidance in Continuous State Spaces. J. Adv. Comput. Intell. Intell. Informatics 11(6): 668-676 (2007) - 2006
- [c5]Daisuke Katagami, Katsumi Nitta, Kazuteru Miyazaki:
Multi User Learning Agent on the Distribution of MDPs. RO-MAN 2006: 698-703 - 2004
- [j3]Kazuteru Miyazaki, Sougo Tsuboi, Shigenobu Kobayashi:
Development of a reinforcement learning system to play Othello. Artif. Life Robotics 7(4): 177-181 (2004) - 2001
- [j2]Kazuteru Miyazaki, Shigenobu Kobayashi:
Rationality of Reward Sharing in Multi-agent Reinforcement Learning. New Gener. Comput. 19(2): 157-172 (2001) - 2000
- [c4]Kazuteru Miyazaki, Shigenobu Kobayashi:
Reinforcement learning for penalty avoiding policy making. SMC 2000: 206-211
1990 – 1999
- 1999
- [c3]Sachiyo Arai, Kazuteru Miyazaki, Shigenobu Kobayashi:
Multi-agent Reinforcement Learning for Crane Control Problem: Designing Rewards for Conflict Resolution. ISADS 1999: 310-319 - [c2]Kazuteru Miyazaki, Shigenobu Kobayashi:
Rationality of Reward Sharing in Multi-agent Reinforcement Learning. PRIMA 1999: 111-125 - 1997
- [j1]Kazuteru Miyazaki, Masayuki Yamamura, Shigenobu Kobayashi:
k-Certainty Exploration Method: An Action Selector to Identify the Environment in Reinforcement Learning. Artif. Intell. 91(1): 155-171 (1997) - [c1]Hajime Kimura, Kazuteru Miyazaki, Shigenobu Kobayashi:
Reinforcement Learning in POMDPs with Function Approximation. ICML 1997: 152-160
Coauthor Index
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