High-quality Synthetic Data is Efficient for Model-based Offline Reinforcement Learning-Reference-Cited by-同舟云学术

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High-quality Synthetic Data is Efficient for Model-based Offline Reinforcement Learning

Published:2024-06-30 Issue: Volume:34 Page:1-7
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Zhang Qichao¹,Fang Xing¹,Xu Kaixuan²,Zhao Weixin³,Li Haoran¹,Zhao Dongbin¹

Affiliation:

1. Chinese Academy of Sciences,The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation,Beijing,China,100190

2. University of Chinese Academy of Sciences,School of Artificial Intelligence,Beijing,China,100049

3. ZHEJIANG SUPCON TECHNOLOGY CO., LTD,Hangzhou,China,310059

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651451.pdf?arnumber=10651451

Reference42 articles.

1. Dynamic-Horizon Model-Based Value Estimation With Latent Imagination

2. Reinforcement Learning and Deep Learning Based Lateral Control for Autonomous Driving [Application Notes]

3. TrajGen: Generating Realistic and Diverse Trajectories With Reactive and Feasible Agent Behaviors for Autonomous Driving

4. Semi-markov offline reinforcement learning for healthcare;Fatemi,2022

5. Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment

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