Deterministic generative adversarial imitation learning
Zuo, Guoyu1,2; Chen, Kexin1,2; Lu, Jiahao1,2; Huang, Xiangsheng3
Source PublicationNEUROCOMPUTING
Corresponding AuthorZuo, Guoyu(
AbstractThis paper proposes a deterministic generative adversarial imitation learning method which allows the robot to implement the motion planning task rapidly by learning from the demonstration data without reward function. In our method, the deep deterministic policy gradient method is used as the generator for learning the action policy on the basis of discriminator, and the demonstration data is input into the generator to ensure its stability. Three experiments on the push and pick-and-place tasks are conducted in the gym robotic environment. Results show that the learning speed of our method is much faster than the stochastic generative adversarial imitation learning method, and it can effectively learn from the demonstration data in different states of the task with higher learning stability. The proposed method can complete the motion planning task without environmental reward quickly and improve the stability of the training process. (C) 2020 Elsevier B.V. All rights reserved.
KeywordRobot learning Imitation learning Reinforcement learning GAN DGAIL
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000520855400006
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Document Type期刊论文
Corresponding AuthorZuo, Guoyu
Affiliation1.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
2.Beijing Key Lab Comp Intelligence & Intelligent S, Beijing 100124, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zuo, Guoyu,Chen, Kexin,Lu, Jiahao,et al. Deterministic generative adversarial imitation learning[J]. NEUROCOMPUTING,2020,388:60-69.
APA Zuo, Guoyu,Chen, Kexin,Lu, Jiahao,&Huang, Xiangsheng.(2020).Deterministic generative adversarial imitation learning.NEUROCOMPUTING,388,60-69.
MLA Zuo, Guoyu,et al."Deterministic generative adversarial imitation learning".NEUROCOMPUTING 388(2020):60-69.
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