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arXiv · 2503.08259

Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy

Yu Tang Liu·Afonso Vale·Aamir Ahmad·Rodrigo Ventura·Meysam Basiri
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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/.

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Yu Tang Liu, Afonso Vale, Aamir Ahmad, Rodrigo Ventura, Meysam Basiri. 2025-03-11. Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy. https://arxiv.org/abs/2503.08259

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