Neurodynamics Adaptive Reward and Action for Hand-to-Eye Calibration With Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Neurodynamics Adaptive Reward and Action for Hand-to-Eye Calibration With Deep Reinforcement Learning

Author:

Zheng Zheng¹^ORCID,Yu Mengfei¹,Guo Pengfei²^ORCID,Zeng Delu³^ORCID

Affiliation:

1. School of Mathematics, South China University of Technology, Guangzhou, China

2. School of Computational Science, Zhongkai University of Agriculture and Engineering, Guangzhou, China

3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

Funder

Fundamental Research Program 401 of Guangdong, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference42 articles.

1. Playing Atari with deep reinforcement learning;mnih;arXiv 1312 5602,2013

4. Policy invariance under reward transformations: Theory and application to reward shaping;ng;Proc Int Conf Mach Learn,1999

5. Deterministic policy gradient algorithms;silver;Proc Int Conf Mach Learn,2014

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