Mjx: A framework for Mahjong AI research. - Sotetsu Koyamada; Keigo Habara; Nao Goto; Shinri Okano; Soichiro Nishimori; Shin Ishii (CoG, 2022)

Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning. - Sotetsu Koyamada; Shinri Okano; Soichiro Nishimori; Yu Murata; Keigo Habara; Haruka Kita; Shin Ishii (NeurIPS, 2023)

Pgx: Hardware-accelerated parallel game simulation for reinforcement learning. - Sotetsu Koyamada; Shinri Okano; Soichiro Nishimori; Yu Murata; Keigo Habara; Haruka Kita; Shin Ishii (CoRR, 2023)

End-to-End Policy Gradient Method for POMDPs and Explainable Agents. - Soichiro Nishimori; Sotetsu Koyamada; Shin Ishii (CoRR, 2023)

A Batch Sequential Halving Algorithm without Performance Degradation. - Sotetsu Koyamada; Soichiro Nishimori; Shin Ishii (RLJ, 2024)

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. - Toshinori Kitamura; Tadashi Kozuno; Masahiro Kato; Yuki Ichihara; Soichiro Nishimori; Akiyoshi Sannai; Sho Sonoda; Wataru Kumagai; Yutaka Matsuo (CoRR, 2024)

Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. - Soichiro Nishimori; Xin-Qiang Cai; Johannes Ackermann; Masashi Sugiyama (CoRR, 2024)

On Symmetric Losses for Robust Policy Optimization with Noisy Preferences. - Soichiro Nishimori; Yu-Jie Zhang; Thanawat Lodkaew; Masashi Sugiyama (CoRR, 2025)

Recursive Reward Aggregation. - Yuting Tang; Yivan Zhang; Johannes Ackermann; Yu-Jie Zhang; Soichiro Nishimori; Masashi Sugiyama (CoRR, 2025)