Topics

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

In reinforcement learning (RL), agents improve their ability through interaction with an environment. The goal of RL in games is to make good agents without giving domain knowledge. AlphaZero is such an application of RL into games.

Computer Go.

Papers

• Murakami, K. and Kaneko, T. “Static and intrinsic diversity in go agents,” Advances in computer games, acg, pp. 113–124. 2025, doi: 10.1007/978-3-032-23657-9_10
• Mandai, Y. and T. Kaneko “RankNet for evaluation functions of the game of Go,” ICGA Jour- nal, Vol. 41, No. 2, pp. 78–91 (2019), DOI: 10.3233/ICG-190108.
• Evaluation of Game Tree Search Methods by Game Records Takeuchi, S.; Kaneko, T.; Yamaguchi, K.; IEEE Transactions on Computational Intelligence and AI in Games, 2 (4), 288 - 302, Dec. 2010.
• H. Yoshimoto, K. Yoshizoe, T. Kaneko, A. Kishimoto, and K. Taura: Monte Carlo Go Has a Way to Go, Twenty-First National Conference on Artificial Intelligence (AAAI-06), pages 1070-1075, 2006

Migo

• https://github.com/tkaneko/migo

Learning algorithms

• Demura, Y. and Kaneko, T. “Initial state diversification for efficient alphazero-style training,” ICGA Journal, 46(2) pp. 40-66. 2024, doi: 10.3233/ICG-240255.

Reinforcement Learning in Half Field Offense

Half field offense RoboCup 2D Soccer (HFO) is a subtask in RoboCup simulated soccer. Reinforcement learning in HFO is a challenging research topic. We developed hierarchical advantage and present HA-PPO to properly handle parameterized actions (e.g., turn (action) 30 degrees (parameter)) in PPO. The successful goal rate on one v.s. one task (agent v.s. the built-in goal keeper) is about 71% that is the best achieved by reinfoncement learning agents.

Parallel or distributed search

• S. Yokoyama, T. Kaneko, and T. Tetsuro: Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search, The 14th International Conference on Advances in Computers and Games
• Scalable Distributed Monte-Carlo Tree Search. Kazuki Yoshizoe, Akihiro Kishimoto, Tomoyuki Kaneko, Haruhiro Yoshimoto and Yutaka Ishikawa. In Proceedings of the 4th Symposium on Combinatorial Search (SoCS'2011), pages 180-187, 2011

Monte-Carlo tree search

• Y. Mandai and T. Kaneko: LinUCB Applied to Monte Carlo Tree Search, Theoretical Computer Science. Volume 644, 6 September 2016, pp.114-126.
• T. Imagawa and T. Kaneko: Monte Carlo Tree Search with Robust Exploration, LNCS, Computers and Games 2016