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
Catan is a multi-player imperfect-information game, involving negotiation among players.
- 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
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),
- 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).
- 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.
- Honghai, L. and T. Kaneko “Training Japanese Mahjong Agent with Two Dimension Feature Representation,” in 25th Game Programming Workshop, pp. 125–130 (2020).
- 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.