# Cooperative Tuning of Multi-Agent Optimal Control Systems

arXiv: https://arxiv.org/abs/2209.12017 · HTML: https://mlsanity.com/paper/2209.12017/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [Adaptive Policy Learning to Additional Tasks](https://mlsanity.com/paper/2305.15193/) (2023)
- [A Control-Barrier-Function-Based Algorithm for Policy Adaptation in Reinforcement Learning](https://mlsanity.com/paper/2510.02720/) (2025)

## Abstract (arXiv)

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.