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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25063 |
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| _version_ | 1866911310661287936 |
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| author | Xu, Nikki Tran, Hien |
| author_facet | Xu, Nikki Tran, Hien |
| contents | Controllers designed with reinforcement learning can be sensitive to model mismatch. We demonstrate that designing such controllers in a virtual simulation environment with an inaccurate model is not suitable for deployment in a physical setup. Controllers designed using an accurate model is robust against disturbance and small mismatch between the physical setup and the mathematical model derived from first principles; while a poor model results in a controller that performs well in simulation but fails in physical experiments. Sensitivity analysis is used to justify these discrepancies and an empirical region of attraction estimation help us visualize their robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Control Synthesis with Reinforcement Learning: A Modeling Perspective Xu, Nikki Tran, Hien Systems and Control Controllers designed with reinforcement learning can be sensitive to model mismatch. We demonstrate that designing such controllers in a virtual simulation environment with an inaccurate model is not suitable for deployment in a physical setup. Controllers designed using an accurate model is robust against disturbance and small mismatch between the physical setup and the mathematical model derived from first principles; while a poor model results in a controller that performs well in simulation but fails in physical experiments. Sensitivity analysis is used to justify these discrepancies and an empirical region of attraction estimation help us visualize their robustness. |
| title | Control Synthesis with Reinforcement Learning: A Modeling Perspective |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.25063 |