Predictive reinforcement learning in non-stationary environments using weighted mixture policy-Reference-Cited by-同舟云学术

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Predictive reinforcement learning in non-stationary environments using weighted mixture policy

Published:2024-03 Issue: Volume:153 Page:111305
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Pourshamsaei Hossein^ORCID,Nobakhti Amin^ORCID

Publisher

Elsevier BV

Reference58 articles.

1. Multi-agent deep reinforcement learning for large-scale traffic signal control;Chu;IEEE Trans. Intell. Transp. Syst.,2020

2. Towards efficient detection and optimal response against sophisticated opponents;Yang,2019

3. A survey of reinforcement learning algorithms for dynamically varying environments;Padakandla;ACM Comput. Surv.,2021

4. Towards continual reinforcement learning: A review and perspectives;Khetarpal;J. Artificial Intelligence Res.,2022

5. Optimizing for the future in non-stationary mdps;Chandak,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using Weighted Mixture Policy Prior Achieving Individual Optimal Policies in Nonstationary Environments;2024 10th International Conference on Artificial Intelligence and Robotics (QICAR);2024-02-29

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