| 2019 | IJCNN | Deep Reinforcement Learning with Dual Targeting Algorithm. | Naoki Kodama, Taku Harada, Kazuteru Miyazaki |
| 2018 | ICMLA | 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 |
| 2018 | PRIMA | Proposal of Detour Path Suppression Method in PS Reinforcement Learning and Its Application to Altruistic Multi-agent Environment. | Daisuke Shiraishi, Kazuteru Miyazaki, Hiroaki Kobayashi |
| 2016 | EUMAS | Proposal of an Action Selection Strategy with Expected Failure Probability and Its Evaluation in Multi-agent Reinforcement Learning. | Kazuteru Miyazaki, Koudai Furukawa, Hiroaki Kobayashi |
| 2012 | ACIIDS | Evaluation of the Improved Penalty Avoiding Rational Policy Making Algorithm in Real World Environment. | Kazuteru Miyazaki, Masaki Itou, Hiroaki Kobayashi |
| 2010 | IDEAL | The Penalty Avoiding Rational Policy Making Algorithm in Continuous Action Spaces. | Kazuteru Miyazaki |
| 2008 | IDEAL | Proposal of Exploitation-Oriented Learning PS-r#. | Kazuteru Miyazaki, Shigenobu Kobayashi |
| 2006 | RO-MAN | Multi User Learning Agent on the Distribution of MDPs. | Daisuke Katagami, Katsumi Nitta, Kazuteru Miyazaki |
| 2000 | SMC | Reinforcement learning for penalty avoiding policy making. | Kazuteru Miyazaki, Shigenobu Kobayashi |
| 1999 | ISADS | Multi-agent Reinforcement Learning for Crane Control Problem: Designing Rewards for Conflict Resolution. | Sachiyo Arai, Kazuteru Miyazaki, Shigenobu Kobayashi |
| 1999 | PRICAI | Rationality of Reward Sharing in Multi-agent Reinforcement Learning. | Kazuteru Miyazaki, Shigenobu Kobayashi |
| 1997 | ICML | Reinforcement Learning in POMDPs with Function Approximation. | Hajime Kimura, Kazuteru Miyazaki, Shigenobu Kobayashi |