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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/2502.19569 |
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| _version_ | 1866918187541463040 |
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| author | Pustilnik, Mark Borrelli, Francesco |
| author_facet | Pustilnik, Mark Borrelli, Francesco |
| contents | In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper presents a novel method based on the Mixed Complementarity Problem (MCP) formulation to compute non-normalized GNE, expanding the solution space. We also propose a systematic approach for selecting the optimal GNE based on predefined criteria, enhancing practical flexibility. Numerical examples illustrate the methods effectiveness, offering an alternative to traditional normalized solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19569 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalized Nash Equilibrium Solutions in Dynamic Games With Shared Constraints Pustilnik, Mark Borrelli, Francesco Robotics In dynamic games with shared constraints, Generalized Nash Equilibria (GNE) are often computed using the normalized solution concept, which assumes identical Lagrange multipliers for shared constraints across all players. While widely used, this approach excludes other potentially valuable GNE. This paper presents a novel method based on the Mixed Complementarity Problem (MCP) formulation to compute non-normalized GNE, expanding the solution space. We also propose a systematic approach for selecting the optimal GNE based on predefined criteria, enhancing practical flexibility. Numerical examples illustrate the methods effectiveness, offering an alternative to traditional normalized solutions. |
| title | Generalized Nash Equilibrium Solutions in Dynamic Games With Shared Constraints |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.19569 |