Conservative In-Distribution Q-Learning for Offline Reinforcement Learning-Reference-Cited by-同舟云学术

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Conservative In-Distribution Q-Learning for Offline Reinforcement Learning

Published:2024-06-30 Issue: Volume:32 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Shao Zhengdao¹,Zhuang Liansheng¹,Yan Jie²,Chen Liting³

Affiliation:

1. University of Science and Technology of China,China

2. Step.ai,Beijing,China

3. McGill University,Canada

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650768.pdf?arnumber=10650768

Reference21 articles.

1. Batch Reinforcement Learning

2. Offline reinforcement learning: Tutorial, review, and perspectives on open problems;Levine,2020

3. Offline reinforcement learning with implicit q-learning;Kostrikov

4. Offline RL with no OOD actions: In-sample learning via implicit value regularization;Xu

5. State advantage weighting for offline rl;Lyu,2022

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