Naoki Mizukami
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Naoki Mizukami,
a Japanese computer scientist affiliated with the Graduate School of Engineering, University of Tokyo. His research interests include Monte-Carlo tree search and UCT considering opponent models, in particular applied to the game of Mahjong [1], and deep learning.
Selected Publication
- Naoki Mizukami, Yoshimasa Tsuruoka (2015). Building a Computer Mahjong Player Based on Monte Carlo Simulation and Opponent Models. IEEE CIG 2015, pdf
- Naoki Mizukami, Jun Suzuki, Hirotaka Kameko, Yoshimasa Tsuruoka (2017). Exploration Bonuses Based on Upper Confidence Bounds for Sparse Reward Games. Advances in Computer Games 15
- Hirotaka Kameko, Jun Suzuki, Naoki Mizukami, Yoshimasa Tsuruoka (2017). Deep Reinforcement Learning with Hidden Layers on Future States. Computer Games Workshop at IJCAI 2017, pdf
- Keigo Kawamura, Naoki Mizukami, Yoshimasa Tsuruoka (2017). Neural Fictitious Self-Play in Imperfect Information Games with Many Players. Computer Games Workshop at IJCAI 2017, pdf
External Links
References
- ↑ Naoki Mizukami, Yoshimasa Tsuruoka (2015). Building a Computer Mahjong Player Based on Monte Carlo Simulation and Opponent Models. IEEE CIG 2015, pdf
- ↑ Publications of Yoshimasa Tsuruoka
