# Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV

arXiv: https://arxiv.org/abs/2311.06144 · HTML: https://mlsanity.com/paper/2311.06144/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2023.

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

- [Equivariant Reinforcement Learning Frameworks for Quadrotor Low-Level Control](https://mlsanity.com/paper/2502.20500/) (2025)
- [Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments](https://mlsanity.com/paper/2605.19202/) (2026)

## Abstract (arXiv)

By leveraging the underlying structures of the quadrotor dynamics, we propose multi-agent reinforcement learning frameworks to innovate the low-level control of a quadrotor, where independent agents operate cooperatively to achieve a common goal. While single-agent reinforcement learning has been successfully applied in quadrotor controls, training a large monolithic network is often data-intensive and time-consuming. Moreover, achieving agile yawing control remains a significant challenge due to the strongly coupled nature of the quadrotor dynamics. To address this, we decompose the quadrotor dynamics into translational and yawing components and assign collaborative reinforcement learning agents to each part to facilitate more efficient training. Additionally, we introduce regularization terms to mitigate steady-state errors and prevent excessive maneuvers. Benchmark studies, including sim-to-sim transfer verification, demonstrate that our proposed training schemes substantially improve the convergence rate of training, while enhancing flight control performance and stability compared to traditional single-agent approaches.