|
Descripción
|
|
|---|---|
| We extend previous works on stochastic dynamic potential games (DPGs) with constrained continuous state-action spaces, and present a closed-loop analysis when the players are forced to use parametric policies. Our main assumption is that the parameter space must be finite-dimensional. We show that a closed-loop Nash equilibrium of a DPG can be found by solving a related optimal control problem (OCP). This is convenient since solving a single OCP (which is a single-objective problem) is usually much simpler than solving the original set of coupled OCPs that form the game (which is a multiobjective problem). | |
|
Internacional
|
Si |
|
Nombre congreso
|
Learning, Inference and Control of Multi-Agent Systems Workshop on Neural Information Processing (NIPS) |
|
Tipo de participación
|
970 |
|
Lugar del congreso
|
Barcelona |
|
Revisores
|
Si |
|
ISBN o ISSN
|
|
|
DOI
|
|
|
Fecha inicio congreso
|
10/12/2016 |
|
Fecha fin congreso
|
10/12/2016 |
|
Desde la página
|
1 |
|
Hasta la página
|
6 |
|
Título de las actas
|
|