A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916688742580224 |
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| author | Zhou, Mo Lu, Jianfeng |
| author_facet | Zhou, Mo Lu, Jianfeng |
| contents | We consider policy gradient methods for stochastic optimal control problem in continuous time. In particular, we analyze the gradient flow for the control, viewed as a continuous time limit of the policy gradient method. We prove the global convergence of the gradient flow and establish a convergence rate under some regularity assumptions. The main novelty in the analysis is the notion of local optimal control function, which is introduced to characterize the local optimality of the iterate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_05816 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee Zhou, Mo Lu, Jianfeng Optimization and Control Machine Learning Systems and Control 93E20 (Primary), 49L12 49M05 (secondary) We consider policy gradient methods for stochastic optimal control problem in continuous time. In particular, we analyze the gradient flow for the control, viewed as a continuous time limit of the policy gradient method. We prove the global convergence of the gradient flow and establish a convergence rate under some regularity assumptions. The main novelty in the analysis is the notion of local optimal control function, which is introduced to characterize the local optimality of the iterate. |
| title | A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee |
| topic | Optimization and Control Machine Learning Systems and Control 93E20 (Primary), 49L12 49M05 (secondary) |
| url | https://arxiv.org/abs/2302.05816 |