This paper focuses on the role of a government of a large population of interacting agents as a meanfield optimal control problem derived from deterministic finite agent dynamics. The control problems are constrained by a Partial Differential Equation of continuity-type without diffusion, governing the dynamics of the probability distribution of the agent population. We derive existence of optimal controls in a measure-theoretical setting as natural limits of finite agent optimal controls without any assumption on the regularity of control competitors. In particular, we prove the consistency of mean-field optimal controls with corresponding underlying finite agent ones. The results follow from a Γ-convergence argument constructed over the m...
AbstractIn this paper we study a mean field model for discrete time, finite number of states, dynami...
We consider mean field games in a large population of heterogeneous agents subject to convex constra...
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-...
This paper focuses on the role of a government of a large population of interacting agents as a mean...
We introduce the concept of mean-field optimal control which is the rigorous limit process connectin...
In this paper we model the role of a government of a large population as a mean field optimal contro...
We examine mean field control problems on a finite state space, in continuous time and over a finite...
We introduce the rigorous limit process connecting finite dimensional sparse optimal control problem...
In this paper we model the role of a government of a large population as a mean field optimal contro...
We examine mean field control problems on a finite state space, in continuous time and over a finite...
A mean-field selective optimal control problem of multipopulation dynamics via transient leadership ...
This paper considers decentralized control and optimization methodologies for large populations of s...
This paper considers decentralized control and optimization methodologies for large populations of s...
This paper considers decentralized control and optimization methodologies for large populations of s...
We derive a framework to compute optimal controls for problems with states in the space of probabili...
AbstractIn this paper we study a mean field model for discrete time, finite number of states, dynami...
We consider mean field games in a large population of heterogeneous agents subject to convex constra...
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-...
This paper focuses on the role of a government of a large population of interacting agents as a mean...
We introduce the concept of mean-field optimal control which is the rigorous limit process connectin...
In this paper we model the role of a government of a large population as a mean field optimal contro...
We examine mean field control problems on a finite state space, in continuous time and over a finite...
We introduce the rigorous limit process connecting finite dimensional sparse optimal control problem...
In this paper we model the role of a government of a large population as a mean field optimal contro...
We examine mean field control problems on a finite state space, in continuous time and over a finite...
A mean-field selective optimal control problem of multipopulation dynamics via transient leadership ...
This paper considers decentralized control and optimization methodologies for large populations of s...
This paper considers decentralized control and optimization methodologies for large populations of s...
This paper considers decentralized control and optimization methodologies for large populations of s...
We derive a framework to compute optimal controls for problems with states in the space of probabili...
AbstractIn this paper we study a mean field model for discrete time, finite number of states, dynami...
We consider mean field games in a large population of heterogeneous agents subject to convex constra...
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-...