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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.01056 |
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| _version_ | 1866912995839311872 |
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| author | Sun, Weihao Xu, Gehui Moreschini, Alessio Parisini, Thomas Malikopoulos, Andreas A. |
| author_facet | Sun, Weihao Xu, Gehui Moreschini, Alessio Parisini, Thomas Malikopoulos, Andreas A. |
| contents | In this paper, we develop a kernel-based policy iteration functional learning framework for computing team-optimal strategies in traffic coordination problems. We consider a multi-agent discrete-time linear system with a cost function that combines quadratic regulation terms and nonlinear safety penalties. Building on the Hilbert space formulation of offline receding-horizon policy iteration, we seek approximate solutions within a reproducing kernel Hilbert space, where the policy improvement step is implemented via a discrete Fréchet derivative. We further study the model-free receding-horizon scenario, where the system dynamics are estimated using recursive least squares, followed by updating the policy using rolling online data. The proposed method is tested in signal-free intersection scenarios via both model-based and model-free simulations and validated in SUMO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01056 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Functional Learning Approach for Team-Optimal Traffic Coordination Sun, Weihao Xu, Gehui Moreschini, Alessio Parisini, Thomas Malikopoulos, Andreas A. Systems and Control In this paper, we develop a kernel-based policy iteration functional learning framework for computing team-optimal strategies in traffic coordination problems. We consider a multi-agent discrete-time linear system with a cost function that combines quadratic regulation terms and nonlinear safety penalties. Building on the Hilbert space formulation of offline receding-horizon policy iteration, we seek approximate solutions within a reproducing kernel Hilbert space, where the policy improvement step is implemented via a discrete Fréchet derivative. We further study the model-free receding-horizon scenario, where the system dynamics are estimated using recursive least squares, followed by updating the policy using rolling online data. The proposed method is tested in signal-free intersection scenarios via both model-based and model-free simulations and validated in SUMO. |
| title | A Functional Learning Approach for Team-Optimal Traffic Coordination |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.01056 |