Prototypical Context-Aware Dynamics for Generalization in Visual Control With Model-Based Reinforcement Learning-Reference-Cited by-同舟云学术

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Prototypical Context-Aware Dynamics for Generalization in Visual Control With Model-Based Reinforcement Learning

Published:2024-09 Issue:9 Volume:20 Page:10717-10727
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Junjie¹^ORCID,Zhang Qichao¹^ORCID,Mu Yao²^ORCID,Li Dong³^ORCID,Zhao Dongbin¹^ORCID,Zhuang Yuzheng³^ORCID,Luo Ping²^ORCID,Wang Bin³^ORCID,Hao Jianye⁴^ORCID

Affiliation:

1. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. University of Hong Kong, Hong Kong, China

3. Noah's Ark Lab, Huawei Technologies, Beijing, China

4. College of Intelligence and Computing, Tianjin University, Tianjin, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

CAS for Grand Challenges

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10530946.pdf?arnumber=10530946

Reference38 articles.

1. Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning

2. Dream to control: Learning behaviors by latent imagination;Hafner,2020

3. Mastering Atari with discrete world models;Hafner,2021

4. DreamerPro: Reconstruction-free model-based reinforcement learning with prototypical representations;Deng,2022

5. Recurrent world models facilitate policy evolution;Ha,2018

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