Solving Decision-Dependent Games by Learning from Feedback
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929319978205184 |
|---|---|
| author | Wood, Killian Zamzam, Ahmed Dall'Anese, Emiliano |
| author_facet | Wood, Killian Zamzam, Ahmed Dall'Anese, Emiliano |
| contents | This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed, which initially involves estimating the distributional dependence on decision variables, and subsequently optimizing over the estimated distributional map. The paper presents guarantees for the approximation of the cost of each agent. Furthermore, a stochastic gradient-based algorithm is developed and analyzed for finding the Nash equilibrium in a distributed fashion. Numerical simulations are provided for a novel electric vehicle charging market formulation using real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17471 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Solving Decision-Dependent Games by Learning from Feedback Wood, Killian Zamzam, Ahmed Dall'Anese, Emiliano Systems and Control Optimization and Control This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed, which initially involves estimating the distributional dependence on decision variables, and subsequently optimizing over the estimated distributional map. The paper presents guarantees for the approximation of the cost of each agent. Furthermore, a stochastic gradient-based algorithm is developed and analyzed for finding the Nash equilibrium in a distributed fashion. Numerical simulations are provided for a novel electric vehicle charging market formulation using real-world data. |
| title | Solving Decision-Dependent Games by Learning from Feedback |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2312.17471 |