Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy
Abstract
Quadcopter attitude control involves two tasks: smooth attitude tracking and aggressive stabilization from arbitrary states. Although both can be formulated as tracking problems, their distinct state spaces and control strategies complicate a unified reward function. We propose a multitask deep reinforcement learning framework that leverages parallel simulation with IsaacGym and a Graph Convolutional Network (GCN) policy to address both tasks effectively. Our multitask Soft Actor-Critic (SAC) approach achieves faster, more reliable learning and higher sample efficiency than single-task methods. We validate its real-world applicability by deploying the learned policy - a compact two-layer network with 24 neurons per layer - on a Pixhawk flight controller, achieving 400 Hz control without extra computational resources. We provide our code at https://github.com/robot-perception-group/GraphMTSAC\_UAV/.
Cite
@article{arxiv.2503.08259,
title = {Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy},
author = {Yu Tang Liu and Afonso Vale and Aamir Ahmad and Rodrigo Ventura and Meysam Basiri},
journal= {arXiv preprint arXiv:2503.08259},
year = {2025}
}