Various games

Catan

Catan is a multi-player imperfect-information game, involving negotiation among players.

• Noguchi, R. and Kaneko, T. “Learning integrated strategies for four-player catan,” IEEE international conference on games. 2026 (in printing)
  • https://github.com/nogulong/rust-catan-rl
• Gendre, Q. and T. Kaneko “Playing Catan with Cross-Dimensional Neural Network,” in ICONIP, pp. 580–592: Springer (2020a), DOI: 10.1007/978-3-030-63833-7_49

2048

• Kaneko, T. and Yamashita, S. “Strongly solving 2048 4x3,” ICGA Journal 48(1) pp. 16-28, 2026, doi: 10.1177/13896911261443437, arxiv:2510.04580
  • https://github.com/tkaneko/db2048-4x3
  • Enumerated legal states of 4x3 variant of 2048, for each state age that is the summation of number tiles.

    

Rogue

We study many intresting properties in reinforcement learning in Rogue, including limited observation, exploration in maze, survival, and generalization.

• Kanagawa, Y. and T. Kaneko “Rogue-Gym: A New Challenge for Generalization in Reinforce- ment Learning,” in IEEE Conference on Games (CoG), pp. 1–8 (2019), DOI: 10.1109/CIG.2019.8848075
• https://github.com/kngwyu/rogue-gym

Ceramic

Azul is a multi-player perfect-information game.

• Gendre, Q. and T. Kaneko “Ceramic: A research environment based on the multi-player strate- gic board game Azul,” in 25th Game Programming Workshop, pp. 155–160, 11 (2020),
• https://github.com/Swynfel/ceramic

StarCraft II

• Xu, F. and Kaneko, T. “Enhanced imagination and feature balancing for cooperative multi-agent reinforcement learning,” Neural information processing, iconip 2024 2284 pp. 153-170, 2025, doi: 10.1007/978-981-96-6954-7_11
• Xu, F. and Kaneko, T. “Curiosity-driven exploration for cooperative multi-agent reinforcement learning” IEEE international joint conference on neural networks pp. 1-8, 2023, doi: 10.1109/IJCNN54540.2023.10191336
• Xu, F. and Kaneko, T. “Local coordination in multi-agent reinforcement learning,” International conference on technologies and applications of artificial intelligence 2021(to appear)
• Hu, Z. and T. Kaneko “Enhancing Sample Efficiency of Deep Reinforcement Learning to Master the Mini-games of StarCraft II,” in 24th Game Programming Workshop, pp. 250–257 (2019).

Geister

• Chen, C. and T. Kaneko “Acquiring Strategies for the Board Game Geister by Regret Minimiza- tion,” in International Conference on Technologies and Applications of Artificial Intelligence, pp. 1–6 (2019), DOI: 10.1109/TAAI48200.2019.8959878.

Mahjong

• H. Long and T. Kaneko “Training Japanese Mahjong Agent with Two Dimension Feature Representation,” in 25th Game Programming Workshop, pp. 125–130 (2020).

Werewolf

• Sun, Y. and T. Kaneko “Prediction of Werewolf Players by Sentiment Analysis of Game Dialogue in Japanese,” (to appear)
• Wang, T. and T. Kaneko “Application of Deep Reinforcement Learning in Werewolf Game Agents,” in IEEE Technologies and Applications of Artificial Intelligence, pp. 28–33 (2018), DOI: 10.1109/TAAI.2018.00016.

Card games

• Yi, C. and Kaneko, T. “Improving counterfactual regret minimization agents training in card game cheat using ordered abstraction” Advances in computers and games 2021(to appear)