• 466 引用
  • 10 h指数
1984 …2019
Pureに変更を加えた場合、すぐここに表示されます。

Fingerprint Toshiharu Sugawaraが取り組む研究トピックをご確認ください。これらのトピックラベルは、この人物の研究に基づいています。これらを共に使用することで、固有の認識が可能になります。

  • 15 同様のプロファイル
Multi agent systems Engineering & Materials Science
Internet Engineering & Materials Science
Reinforcement learning Engineering & Materials Science
Multi-agent Systems Mathematics
Complex networks Engineering & Materials Science
Intelligent agents Engineering & Materials Science
Autonomous agents Engineering & Materials Science
Robots Engineering & Materials Science

ネットワーク 最近の共同研究。丸をクリックして詳細を確認しましょう。

研究成果 1984 2019

  • 466 引用
  • 10 h指数
  • 103 Conference contribution
  • 43 Article
  • 3 Chapter

Analysis of Traffic Congestion Reducer Agents on Multi-Lane Highway

Ishihara, Y. & Sugawara, T., 2019 2 1, Proceedings - 2019 2nd International Conference on Intelligent Autonomous Systems, ICoIAS 2019. Institute of Electrical and Electronics Engineers Inc., p. 135-141 7 p. 8782519. (Proceedings - 2019 2nd International Conference on Intelligent Autonomous Systems, ICoIAS 2019).

研究成果: Conference contribution

Traffic Congestion
Traffic congestion
Congestion
Railroad cars
Autonomous agents

Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions

Miyashita, Y. & Sugawara, T., 2019 1 1, Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Tetko, I. V., Karpov, P., Theis, F. & Kurková, V. (版). Springer-Verlag, p. 541-554 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 11727 LNCS).

研究成果: Conference contribution

Multiagent Learning
Electric insulation coordination
Q-learning
Reinforcement learning
Reinforcement Learning

Coordinated behavior of cooperative agents using deep reinforcement learning

Diallo, E. A. O., Sugiyama, A. & Sugawara, T., 2019 1 1, : : Neurocomputing.

研究成果: Article

Reinforcement learning
Cooperative Behavior
Learning
Network architecture
Reward

Learning of activity cycle length based on battery limitation in multi-agent continuous cooperative patrol problems

Sugiyama, A., Wu, L. & Sugawara, T., 2019 1 1, ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence. Rocha, A., Steels, L. & van den Herik, J. (版). SciTePress, p. 62-71 10 p. (ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence; 巻数 1).

研究成果: Conference contribution

Robots
Intelligent control
Sensors

Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networks

Miura, Y., Toriumi, F. & Sugawara, T., 2019 7 13, GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, p. 297-298 2 p. (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion).

研究成果: Conference contribution

Complex networks
Complex Networks
Genetic algorithms
Genetic Algorithm
Network Analysis