グラフ方策を用いたマルチタスク強化学習によるクアッドコプターの姿勢安定化と追従
Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy
IsaacGymでの並列シミュレーションとグラフ畳み込みネットワーク方策を組み合わせたマルチタスクSACで、クアッドコプターの姿勢追従と安定化を同時に学習し、Pixhawk上で400Hz制御を実現した。
著者: Yu Tang Liu, Afonso Vale, Aamir Ahmad, Rodrigo Ventura, Meysam Basiri
分類: cs.RO
原文アブストラクト
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/.
