Cooperative Tuning of Multi-Agent Optimal Control Systems
This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective functions, or controllers in a coordinated way to minimize the sum of their loss functions. Different from classical techniques for tuning parameters in a controller, we allow tunable parameters appearing in both the system dynamics and the objective functions of each agent. A framework is developed to allow all agents to reach a consensus on the tunable parameter, which minimizes team loss. The key idea of the proposed algorithm rests on the integration of consensus-based distributed optimization for a multi-agent system and a gradient generator capturing the optimal performance as a function of the parameter in the feedback loop tuning the parameter for each agent. Both theoretical results and simulations for a synchronous multi-agent rendezvous problem are provided to validate the proposed method for cooperative tuning of multi-agent optimal control.
Code (1)
Tasks
Distributed OptimizationSimilar Papers 제목 키워드 기반
Cooperative Path Integral Control for Stochastic Multi-Agent Systems
A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and n…
Adaptive Dynamic Programming and Data-Driven Cooperative Optimal Output Regulation with Adaptive Observers
In this paper, a novel adaptive optimal control strategy is proposed to achieve the cooperative optimal output regulation of continuous-time linear multi-agent systems based on adaptive dynamic programming (ADP). The pro…
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation
Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic treatment regimes, distributed control, an…
Federated LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control
Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multiple pre-trained models remains an open challenge. Current approaches lar…
Image RestorationGeneralization of Safe Optimal Control Actions on Networked Multi-Agent Systems
We propose a unified framework to fast generate a safe optimal control action for a new task from existing controllers on Multi-Agent Systems (MASs). The control action composition is achieved by taking a weighted mixtur…