GeneWorker: An end-to-end robotic reinforcement learning approach with collaborative generator and worker networks-Reference-Cited by-同舟云学术

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GeneWorker: An end-to-end robotic reinforcement learning approach with collaborative generator and worker networks

Published:2024-10 Issue: Volume:178 Page:106472
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Wang Hao^ORCID,Man Hengyu^ORCID,Cui Wenxue^ORCID,Lu Riyu^ORCID,Cai Chenxin^ORCID,Fan Xiaopeng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference53 articles.

1. Learning to poke by poking: Experiential learning of intuitive physics;Agrawal;Advances in Neural Information Processing Systems,2016

2. Hindsight experience replay;Andrychowicz;Advances in Neural Information Processing Systems,2017

3. Skill discovery for exploration and planning using deep skill graphs;Bagaria,2021

4. The temporal lobes;Barense,2016

5. Neuronlike adaptive elements that can solve difficult learning control problems;Barto;IEEE Transactions on Systems, Man and Cybernetics,1983

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