# Robust synchronization and policy adaptation for networked heterogeneous agents

arXiv: https://arxiv.org/abs/2409.03273 · HTML: https://mlsanity.com/paper/2409.03273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [A Tour of Reinforcement Learning: The View from Continuous Control](https://mlsanity.com/paper/1806.09460/) (2018)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [Tutorial on dynamic average consensus: the problem, its applications, and the algorithms](https://mlsanity.com/paper/1803.04628/) (2018)
- [Connections Between Adaptive Control and Optimization in Machine Learning](https://mlsanity.com/paper/1904.05856/) (2019)
- [Online Policies for Real-Time Control Using MRAC-RL](https://mlsanity.com/paper/2103.16551/) (2021)
- [Nonasymptotic Convergence Rates for Cooperative Learning Over Time-Varying Directed Graphs](https://mlsanity.com/paper/1410.1977/) (2014)
- [MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty](https://mlsanity.com/paper/2011.10562/) (2020)
- [Adaptive Synchronization of Heterogeneous Multi-Agent Systems: A Free Observer Approach](https://mlsanity.com/paper/2010.02862/) (2020)
- [An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks](https://mlsanity.com/paper/2407.00570/) (2024)

## Abstract (arXiv)

We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings.