A modular framework for stabilizing deep reinforcement learning control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929289046261760 |
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| author | Lawrence, Nathan P. Loewen, Philip D. Wang, Shuyuan Forbes, Michael G. Gopaluni, R. Bhushan |
| author_facet | Lawrence, Nathan P. Loewen, Philip D. Wang, Shuyuan Forbes, Michael G. Gopaluni, R. Bhushan |
| contents | We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kucera parameterization based entirely on input-output exploration data. Using a neural network to express a parameterized set of nonlinear stable operators enables seamless integration with standard deep learning libraries. We demonstrate the approach on a realistic simulation of a two-tank system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03422 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A modular framework for stabilizing deep reinforcement learning control Lawrence, Nathan P. Loewen, Philip D. Wang, Shuyuan Forbes, Michael G. Gopaluni, R. Bhushan Systems and Control Machine Learning We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kucera parameterization based entirely on input-output exploration data. Using a neural network to express a parameterized set of nonlinear stable operators enables seamless integration with standard deep learning libraries. We demonstrate the approach on a realistic simulation of a two-tank system. |
| title | A modular framework for stabilizing deep reinforcement learning control |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2304.03422 |