# Using Soft Actor-Critic for Low-Level UAV Control

arXiv: https://arxiv.org/abs/2010.02293 · HTML: https://mlsanity.com/paper/2010.02293/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control](https://mlsanity.com/paper/2202.09262/) (2022)
- [Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)](https://mlsanity.com/paper/2512.18333/) (2025)
- [Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization](https://mlsanity.com/paper/2209.06332/) (2022)
- [Dynamic Entropy Tuning in Reinforcement Learning Low-Level Quadcopter Control: Stochasticity vs Determinism](https://mlsanity.com/paper/2512.18336/) (2025)
- [Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2210.02964/) (2022)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs), or drones, have recently been used in several civil application domains from organ delivery to remote locations to wireless network coverage. These platforms, however, are naturally unstable systems for which many different control approaches have been proposed. Generally based on classic and modern control, these algorithms require knowledge of the robot's dynamics. However, recently, model-free reinforcement learning has been successfully used for controlling drones without any prior knowledge of the robot model. In this work, we present a framework to train the Soft Actor-Critic (SAC) algorithm to low-level control of a quadrotor in a go-to-target task. All experiments were conducted under simulation. With the experiments, we show that SAC can not only learn a robust policy, but it can also cope with unseen scenarios. Videos from the simulations are available in https://www.youtube.com/watch?v=9z8vGs0Ri5g and the code in https://github.com/larocs/SAC_uav.