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ORCIDTomoyuki Kaneko
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2020 – today
- 2024
[j9]Yosuke Demura, Tomoyuki Kaneko:
Initial state diversification for efficient AlphaZero-style training. J. Int. Comput. Games Assoc. 46(2): 40-66 (2024)- 2023
- [c41]Shanchuan Wan, Yujin Tang, Yingtao Tian, Tomoyuki Kaneko:
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards. IJCAI 2023: 4289-4298 - [c40]Fanchao Xu, Tomoyuki Kaneko:
Curiosity-driven Exploration for Cooperative Multi-Agent Reinforcement Learning. IJCNN 2023: 1-8 - [i4]Shanchuan Wan
, Yujin Tang, Yingtao Tian, Tomoyuki Kaneko:
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards. CoRR abs/2304.10770 (2023) - 2022
- [c39]Chen Chen, Tomoyuki Kaneko:
Learning Strategies for Imperfect Information Board Games Using Depth-Limited Counterfactual Regret Minimization and Belief State. CoG 2022: 486-493 - 2021
- [j8]Hanhua Zhu, Tomoyuki Kaneko:
Residual Network for Deep Reinforcement Learning with Attention Mechanism. J. Inf. Sci. Eng. 37(3): 517-533 (2021) - [c38]Cheng Yi, Tomoyuki Kaneko:
Improving Counterfactual Regret Minimization Agents Training in Card Game Cheat Using Ordered Abstraction. ACG 2021: 3-13 - [c37]Zhejie Hu, Tomoyuki Kaneko:
Hierarchical Advantage for Reinforcement Learning in Parameterized Action Space. CoG 2021: 1-8 - [c36]Taichi Nakayashiki, Tomoyuki Kaneko:
Maximum Entropy Reinforcement Learning in Two-Player Perfect Information Games. SSCI 2021: 1-8 - [c35]Fanchao Xu, Tomoyuki Kaneko:
Local Coordination in Multi-Agent Reinforcement Learning. TAAI 2021: 149-154 - 2020
- [c34]Quentin Gendre, Tomoyuki Kaneko:
Playing Catan with Cross-Dimensional Neural Network. ICONIP (2) 2020: 580-592 - [c33]Taichi Nakayashiki, Tomoyuki Kaneko:
Evaluation of Loss Function for Stable Policy Learning in Dobutsu Shogi. TAAI 2020: 199-204 - [i3]Quentin Gendre, Tomoyuki Kaneko:
Playing Catan with Cross-dimensional Neural Network. CoRR abs/2008.07079 (2020) - [i2]Yuji Kanagawa, Tomoyuki Kaneko:
Diverse Exploration via InfoMax Options. CoRR abs/2010.02756 (2020)
2010 – 2019
- 2019
- [j7]Yusaku Mandai, Tomoyuki Kaneko:
RankNet for evaluation functions of the game of Go. J. Int. Comput. Games Assoc. 41(2): 78-91 (2019) - [j6]Tomoyuki Kaneko, Takenobu Takizawa:
Computer Shogi Tournaments and Techniques. IEEE Trans. Games 11(3): 267-274 (2019) - [c32]Yuji Kanagawa, Tomoyuki Kaneko:
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning. CoG 2019: 1-8 - [c31]Chen Chen, Tomoyuki Kaneko:
Acquiring Strategies for the Board Game Geister by Regret Minimization. TAAI 2019: 1-6 - [c30]Zhejie Hu, Tomoyuki Kaneko:
Application of Deep-RL with Sample-Efficient Method in Mini-games of StarCraft II. TAAI 2019: 1-6 - [c29]Hanhua Zhu, Tomoyuki Kaneko:
Deep Residual Attention Reinforcement Learning. TAAI 2019: 1-6 - [i1]Yuji Kanagawa, Tomoyuki Kaneko:
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning. CoRR abs/1904.08129 (2019) - 2018
- [c28]Shanchuan Wan, Tomoyuki Kaneko:
Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks. CIG 2018: 1-8 - [c27]Shanchuan Wan, Tomoyuki Kaneko:
Heterogeneous Multi-task Learning of Evaluation Functions for Chess and Shogi. ICONIP (3) 2018: 347-358 - [c26]Hanhua Zhu, Tomoyuki Kaneko:
Comparison of Loss Functions for Training of Deep Neural Networks in Shogi. TAAI 2018: 18-23 - [c25]Tianhe Wang, Tomoyuki Kaneko:
Application of Deep Reinforcement Learning in Werewolf Game Agents. TAAI 2018: 28-33 - [c24]Hyunwoo Oh, Tomoyuki Kaneko:
Deep Recurrent Q-Network with Truncated History. TAAI 2018: 34-39 - [c23]Taichi Nakayashiki, Tomoyuki Kaneko:
Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search. TAAI 2018: 126-131 - [c22]Yusaku Mandai, Tomoyuki Kaneko:
Alternative Multitask Training for Evaluation Functions in Game of Go. TAAI 2018: 132-135 - 2017
- [c21]Shanchuan Wan, Tomoyuki Kaneko:
Imitation Learning for Playing Shogi Based on Generative Adversarial Networks. TAAI 2017: 92-95 - [c20]Takahisa Imagawa, Tomoyuki Kaneko:
Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood. TAAI 2017: 202-207 - 2016
- [j5]Yusaku Mandai, Tomoyuki Kaneko:
LinUCB applied to Monte Carlo tree search. Theor. Comput. Sci. 644: 114-126 (2016) - [c19]Takahisa Imagawa, Tomoyuki Kaneko:
Monte Carlo Tree Search with Robust Exploration. Computers and Games 2016: 34-46 - [c18]Yusaku Mandai, Tomoyuki Kaneko:
Improved LinUCT and its evaluation on incremental random-feature tree. CIG 2016: 1-8 - [c17]Shotaro Omori, Tomoyuki Kaneko:
Learning of Evaluation Functions to Realize Playing Styles in Shogi. PRICAI 2016: 367-379 - 2015
- [c16]Yusaku Mandai, Tomoyuki Kaneko:
LinUCB Applied to Monte-Carlo Tree Search. ACG 2015: 41-52 - [c15]Shu Yokoyama, Tomoyuki Kaneko, Tetsuro Tanaka:
Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search. ACG 2015: 210-222 - [c14]Shogo Takeuchi, Tomoyuki Kaneko:
Estimating Ratings of Computer Players by the Evaluation Scores and Principal Variations in Shogi. ACIT-CSI 2015: 85-90 - [c13]Takahisa Imagawa, Tomoyuki Kaneko:
Enhancements in Monte Carlo tree search algorithms for biased game trees. CIG 2015: 43-50 - 2014
- [j4]Kunihito Hoki, Tomoyuki Kaneko:
Large-Scale Optimization for Evaluation Functions with Minimax Search. J. Artif. Intell. Res. 49: 527-568 (2014) - 2013
- [j3]Hiroyuki Hamada, Fumimasa Nomura, Tomoyuki Kaneko, Kenji Yasuda, Masahiro Okamoto:
Exploring the implicit interlayer regulatory mechanism between cells and tissue: Stochastic mathematical analyses of the spontaneous ordering in beating synchronization. Biosyst. 111(3): 208-215 (2013) - [j2]Kunihito Hoki, Tomoyuki Kaneko, Akihiro Kishimoto, Takeshi Ito:
Parallel Dovetailing and its Application to Depth-First Proof-Number Search. J. Int. Comput. Games Assoc. 36(1): 22-36 (2013) - [c12]Kunihito Hoki, Tomoyuki Kaneko, Daisaku Yokoyama, Takuya Obata, Hiroshi Yamashita, Yoshimasa Tsuruoka, Takeshi Ito:
A System-Design Outline of the Distributed-Shogi-System Akara 2010. SNPD 2013: 466-471 - 2011
- [c11]Tomoyuki Kaneko, Kunihito Hoki:
Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes. ACG 2011: 158-169 - [c10]Kunihito Hoki, Tomoyuki Kaneko:
The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search. ACG 2011: 184-195 - [c9]Yoshiaki Yamaguchi, Kazunori Yamaguchi, Tetsuro Tanaka, Tomoyuki Kaneko:
Infinite Connect-Four Is Solved: Draw. ACG 2011: 208-219 - [c8]Kazuki Yoshizoe, Akihiro Kishimoto, Tomoyuki Kaneko, Haruhiro Yoshimoto, Yutaka Ishikawa:
Scalable Distributed Monte-Carlo Tree Search. SOCS 2011: 180-187 - 2010
- [j1]Shogo Takeuchi, Tomoyuki Kaneko, Kazunori Yamaguchi:
Evaluation of Game Tree Search Methods by Game Records. IEEE Trans. Comput. Intell. AI Games 2(4): 288-302 (2010) - [c7]Tomoyuki Kaneko:
Parallel Depth First Proof Number Search. AAAI 2010: 95-100
2000 – 2009
- 2008
- [c6]Shogo Takeuchi, Tomoyuki Kaneko, Kazunori Yamaguchi:
Evaluation of Monte Carlo tree search and the application to Go. CIG 2008: 191-198 - 2007
- [c5]Shogo Takeuchi, Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai:
Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability. AAAI 2007: 858-863 - 2006
- [c4]Haruhiro Yoshimoto, Kazuki Yoshizoe, Tomoyuki Kaneko, Akihiro Kishimoto, Kenjiro Taura:
Monte Carlo Go Has a Way to Go. AAAI 2006: 1070-1075 - [c3]Shunsuke Soeda, Tomoyuki Kaneko, Tetsuro Tanaka:
Dual Lambda Search and Shogi Endgames. ACG 2006: 126-139 - 2003
- [c2]Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai:
Automated Identification of Patterns in Evaluation Functions. ACG 2003: 279-298 - 2000
- [c1]Tomoyuki Kaneko, Kazunori Yamaguchi, Satoru Kawai:
Compiling Logical Features into Specialized State-Evaluators by Partial Evaluation, Boolean Tables and Incremental Calculation. PRICAI 2000: 72-82
Coauthor Index
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last updated on 2024-11-13 23:52 CET by the dblp team
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