1 |
Xie J, Liu C C. Multi-agent Systems and their Applications[J]. Journal of International Council on Electrical Engineering(S2234-8972), 2017, 7(1): 188-197.
|
2 |
Van Tendeloo Y, Vangheluwe H. Extending the DEVS Formalism with Initialization Information[J]. arXiv preprint arXiv:, 2018.
|
3 |
Seo C, Zeigler B P, Kim D. DEVS Markov Modeling and Simulation: Formal Definition and Implementation[C]//Proceedings of the 4th ACM International Conference of Computing for Engineering and Sciences, 2018: 1-12.
|
4 |
Bae J W, Lee G H, Moon I C. Formal Specification Supporting Incremental and Flexible Agent-based Modeling[C]//Proceedings of the 2012 Winter Simulation Conference (WSC). IEEE, 2012: 1-12.
|
5 |
Müller J P. Towards a Formal Semantics of Event-based Multi-agent Simulations[C]//International Workshop on Multi-Agent Systems and Agent-Based Simulation. Springer, Berlin, Heidelberg, 2008: 110-126.
|
6 |
Barbieri E, Capocchi L, Santucci J F. DEVS Modeling and Simulation of Financial Leverage Effect Based on Markov Decision Process[C]//2018 4th International Conference on Universal Village (UV). IEEE, 2018: 1-5.
|
7 |
Capocchi L, Santucci J F, Zeigler B P. Discrete Event Modeling and Simulation Aspects to Improve Machine Learning Systems[C]//2018 4th International Conference on Universal Village (UV). IEEE, 2018: 1-6.
|
8 |
Kessler C, Capocchi L, Santucci J F, et al. Hierarchical Markov Decision Process based on DEVS Formalism[C]//2017 Winter Simulation Conference (WSC). IEEE, 2017: 1001-1012.
|
9 |
Zhang M. Constructing a Cognitive Agent Model using DEVS Framework for Multi-agent Simulation[C]// Proc. 15th Eur. Agent Syst. Summer School (EASSS), 2013: 1-5.
|
10 |
Akplogan M, Quesnel G, Garcia F, et al. Towards a Deliberative Agent System based on DEVS Formalism for Application in Agriculture[C]//Proceedings of the 2010 Summer Computer Simulation Conference. 2010: 250-257.
|
11 |
Chow A C H, Zeigler B P. Parallel DEVS: A Parallel, Hierarchical, Modular Modeling Formalism[C]//Proceedings of Winter Simulation Conference. IEEE, 1994: 716-722.
|
12 |
孙长银, 穆朝絮. 多智能体深度强化学习的若干关键科学问题[J]. 自动化学报, 2020, 45: 1-12.
|
|
Sun Changyin, Mu Chaoxu. Important Scientific Problems of Multi-agent deep Reinforcement Learning[J]. Acta Automatica Sinica, 2020, 45: 1-12.
|
13 |
Haarnoja T, Zhou A, Abbeel P, et al. Soft Actor-critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor[C]//International Conference on Machine Learning. PMLR, 2018: 1861-1870.
|
14 |
张红旗, 杨峻楠, 张传富. 基于不完全信息随机博弈与 Q-learning 的防御决策方法[J]. 通信学报, 2018, 39(8): 56-68.
|
|
Zhang Hongqi, Yang Junnan, Zhang Chuanfu. Defense Decision-making Method based on Incomplete Information Stochastic Game and Q-learning[J]. Journal on Communications, 2018, 39(8): 56-68.
|
15 |
蒲玮, 李雄. 基于扩展 FIPA-ACL 的装备保障 Agent 通信语言[J]. 系统工程理论与实践, 2018, 38(1): 220-228.
|
|
Pu Wei, Li Xiong. Equipment Support Agent Communication Language based on Extended FIPA-ACL[J]. System Engineering Theory&Practice, 2018, 38(1): 220-228.
|
16 |
梁凯, 陈志军, 闫学勤. 移动机器人路径规划仿真研究[J]. 现代电子技术, 2018, 41(17): 6.
|
|
Liang Kai, Chen Zhijun, Yan Xueqin. Simulation Study on Effective Path Planning for Mobile Robot[J]. Modern Electronics Technique, 2018, 41(17): 6.
|
17 |
陈建平, 邹锋, 刘全, 等. 一种基于生成对抗网络的强化学习算法[J]. 计算机科学, 2019, 46(10): 265-272.
|
|
Chen Jianping, Zou Feng, Liu Quan, etc. Reinforcement Learning Algorithm Based on Generative Adversarial Networks[J]. Computer Science, ,2019, 46(10): 265-272.
|
18 |
Van Tendeloo Y, Vangheluwe H. The Modular Architecture of the Python (P) DEVS Simulation Kernel[C]//Proceedings of the 2014 Symposium on Theory of Modeling and Simulation-DEVS. 2014: 387-392.
|
19 |
Sutton R S, Barto A G. Reinforcement Learning: An Introduction[J]. IEEE Transactions on Neural Networks(S1045-9227), 1998, 9(5): 1054.
|