# Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)

arXiv: https://arxiv.org/abs/2512.18333 · HTML: https://mlsanity.com/paper/2512.18333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 5 references that are in the map.
- First posted on arXiv 20 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 2 of 2)

- [Using Soft Actor-Critic for Low-Level UAV Control](https://mlsanity.com/paper/2010.02293/) (2020)
- [A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure](https://mlsanity.com/paper/2109.10488/) (2021)

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

- [A Heuristic Approach for Performance Tuning in RL-based Quadrotor Control via Reward Design and Termination Conditions](https://mlsanity.com/paper/2605.19166/) (2026)
- [Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters](https://mlsanity.com/paper/2606.02107/) (2026)

## Abstract (arXiv)

This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling the four rotors' RPMs directly, this paper aims to control the quadrotor's thrust vector. The RL agent computes the percentage of overall thrust along the quadrotor's z-axis along with the desired Roll (φ) and Pitch (θ) angles. The agent then sends the calculated control signals along with the current quadrotor's Yaw angle (psi) to an attitude PID controller. The PID controller then maps the control signals to motor RPMs. The Soft Actor-Critic algorithm, a model-free off-policy stochastic RL algorithm, was used to train the RL agents. Training results show the faster training time of the proposed thrust vector controller in comparison to the conventional RPM controllers. Simulation results show smoother and more accurate path-following for the proposed thrust vector controller.