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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.12235 |
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| _version_ | 1866917572596727808 |
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| author | Deng, Bairong Yu, Tao Pan, Zhenning Zhang, Xuehan Wu, Yufeng Ding, Qiaoyi |
| author_facet | Deng, Bairong Yu, Tao Pan, Zhenning Zhang, Xuehan Wu, Yufeng Ding, Qiaoyi |
| contents | Reinforcement learning is an emerging approaches to facilitate multi-stage sequential decision-making problems. This paper studies a real-time multi-stage stochastic power dispatch considering multivariate uncertainties. Current researches suffer from low generalization and practicality, that is, the learned dispatch policy can only handle a specific dispatch scenario, its performance degrades significantly if actual samples and training samples are inconsistent. To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch policy is proposed. Specifically, a more general contextual Markov decision process (MDP) and scalable graph representation are introduced to achieve a more generalized multi-stage stochastic power dispatch modeling. An upper meta-learner is proposed to encode context for different dispatch scenarios and learn how to achieve dispatch task identification while the lower policy learner learns context-specified dispatch policy. After sufficient offline learning, this approach can rapidly adapt to unseen and undefined scenarios with only a few updations of the hypothesis judgments generated by the meta-learner. Numerical comparisons with state-of-the-art policies and traditional reinforcement learning verify the optimality, efficiency, adaptability, and scalability of the proposed Meta-GRL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning Deng, Bairong Yu, Tao Pan, Zhenning Zhang, Xuehan Wu, Yufeng Ding, Qiaoyi Machine Learning Systems and Control Reinforcement learning is an emerging approaches to facilitate multi-stage sequential decision-making problems. This paper studies a real-time multi-stage stochastic power dispatch considering multivariate uncertainties. Current researches suffer from low generalization and practicality, that is, the learned dispatch policy can only handle a specific dispatch scenario, its performance degrades significantly if actual samples and training samples are inconsistent. To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch policy is proposed. Specifically, a more general contextual Markov decision process (MDP) and scalable graph representation are introduced to achieve a more generalized multi-stage stochastic power dispatch modeling. An upper meta-learner is proposed to encode context for different dispatch scenarios and learn how to achieve dispatch task identification while the lower policy learner learns context-specified dispatch policy. After sufficient offline learning, this approach can rapidly adapt to unseen and undefined scenarios with only a few updations of the hypothesis judgments generated by the meta-learner. Numerical comparisons with state-of-the-art policies and traditional reinforcement learning verify the optimality, efficiency, adaptability, and scalability of the proposed Meta-GRL. |
| title | Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2401.12235 |