T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler
Author:
Affiliation:
1. College of Intelligence and Computing, Tianjin University,Tianjin,China
2. University of Alberta and Alberta Machine Intelligence Institute,Department of Computing Science,Edmonton,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191536.pdf?arnumber=10191536
Reference37 articles.
1. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning;yu;Proceedings of the Conference on Robot Learning ser Proceedings of Machine Learning Research,2020
2. Actor-mimic: Deep multitask and transfer reinforcement learning;parisotto;4th International Conference on Learning Representations ICLR 2016 San Juan Puerto Rico May 2–4 2016 Conference Track Proceedings,2016
3. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
4. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
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