From open-loop representations to closed-loop feedback implementations in differential games: A numerical case study
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911652458266624 |
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| author | Braun, Philipp Molloy, Timothy L. Barkai, Gal Shames, Iman |
| author_facet | Braun, Philipp Molloy, Timothy L. Barkai, Gal Shames, Iman |
| contents | Solutions to pursuit-evasion and surveillance-evasion differential games are typically computed and expressed using open-loop representations, with the synthesis of feedback strategies significantly less common. We propose a numerical scheme for obtaining feedback strategies for the recently introduced prying-pedestrian surveillance-evasion differential game. The scheme involves computing feedback strategies as input-output maps approximated via neural networks trained using data obtained from open-loop representations of solutions. Simulations show the effectiveness of neural networks trained with an appropriate learning-loss function. Since optimal feedback strategies are discontinuous, as a second contribution, the potential loss/gain of individual players is subsequently studied for players using sample-and-hold feedback compared to continuous-time feedback. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04768 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From open-loop representations to closed-loop feedback implementations in differential games: A numerical case study Braun, Philipp Molloy, Timothy L. Barkai, Gal Shames, Iman Systems and Control Solutions to pursuit-evasion and surveillance-evasion differential games are typically computed and expressed using open-loop representations, with the synthesis of feedback strategies significantly less common. We propose a numerical scheme for obtaining feedback strategies for the recently introduced prying-pedestrian surveillance-evasion differential game. The scheme involves computing feedback strategies as input-output maps approximated via neural networks trained using data obtained from open-loop representations of solutions. Simulations show the effectiveness of neural networks trained with an appropriate learning-loss function. Since optimal feedback strategies are discontinuous, as a second contribution, the potential loss/gain of individual players is subsequently studied for players using sample-and-hold feedback compared to continuous-time feedback. |
| title | From open-loop representations to closed-loop feedback implementations in differential games: A numerical case study |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2605.04768 |