Sample-Efficient Multimodal Dynamics Modeling for Risk-Sensitive Reinforcement Learning

被引：0
|
作者：
Yashima, Ryota [1 ]
Yamaguchi, Akihiko [1 ]
Hashimoto, Koichi [1 ]
机构：
[1] Tohoku University, Graduate School of Information Sciences, Miyagi, Japan
来源：
2022 8th International Conference on Mechatronics and Robotics Engineering, ICMRE 2022 | 2022年
关键词：
Compendex;
D O I：
8th International Conference on Mechatronics and Robotics Engineering, ICMRE 2022
中图分类号：
学科分类号：
摘要：
Stochastic models
引用
收藏
页码：21 / 27
相关论文
共 50 条
  • [41] Risk-sensitive Inverse Reinforcement Learning via Coherent Risk Models
    Majumdar, Anirudha
    Singh, Sumeet
    Mandlekar, Ajay
    Pavone, Marco
    ROBOTICS: SCIENCE AND SYSTEMS XIII, 2017,
  • [42] RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
    Qiu, Wei
    Wang, Xinrun
    Yu, Runsheng
    He, Xu
    Wang, Rundong
    An, Bo
    Obraztsova, Svetlana
    Rabinovich, Zinovi
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [43] Risk-sensitive reinforcement learning algorithms with generalized average criterion
    殷苌茗
    王汉兴
    赵飞
    Applied Mathematics and Mechanics(English Edition), 2007, (03) : 405 - 416
  • [44] State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning
    Ma, Shuai
    Yu, Jia Yuan
    THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 4512 - 4519
  • [45] Risk-sensitive reinforcement learning algorithms with generalized average criterion
    Chang-ming Yin
    Wang Han-xing
    Zhao Fei
    Applied Mathematics and Mechanics, 2007, 28 : 405 - 416
  • [46] Gradient-Based Inverse Risk-Sensitive Reinforcement Learning
    Mazumdar, Eric
    Ratliff, Lillian J.
    Fiez, Tanner
    Sastry, S. Shankar
    2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017,
  • [47] Risk-Sensitive Reinforcement Learning via Policy Gradient Search
    Prashanth, L. A.
    Fu, Michael C.
    FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2022, 15 (05): : 537 - 693
  • [48] Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
    Wang, Bingyan
    Yan, Yuling
    Fan, Jianqing
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [49] Risk-sensitive reinforcement learning applied to control under constraints
    Geibel, P
    Wysotzki, F
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2005, 24 : 81 - 108
  • [50] Risk-sensitive reinforcement learning applied to control under constraints
    Geibel, P. (PGEIBEL@UOS.DE), 1600, American Association for Artificial Intelligence (24):
12345