mlsanityData to 14 Oct 2026

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

arXiv 2503.08259 ↗First posted 11 Mar 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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/GraphMTSACUAV/.

Key facts. No citing papers in the map yet (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 24 of its 30 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

24 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Multi-Task Reinforcement Learning for Quadrotors17 Dec

Other references · 23

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  4. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  5. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  6. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  7. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  8. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  9. 2016Learning to reinforcement learncited by 377
  10. 2021Multi-Task Reinforcement Learning with Context-based Representationscited by 174
  11. 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 167
  12. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 143
11 more
  1. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 99
  2. 2018GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learningcited by 90
  3. 2020Flightmare: A Flexible Quadrotor Simulatorcited by 84
  4. 2023Multi-Task Reinforcement Learning with Mixture of Orthogonal Expertscited by 49
  5. 2017Divide-and-Conquer Reinforcement Learningcited by 48
  6. 2022PaCo: Parameter-Compositional Multi-Task Reinforcement Learningcited by 46
  7. 2024Sharing Knowledge in Multi-Task Deep Reinforcement Learningcited by 44
  8. 2021Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environmentscited by 25
  9. 2024A Learning-based Quadcopter Controller with Extreme Adaptationcited by 17
  10. 2023Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learningcited by 10
  11. 2024Task and Domain Adaptive Reinforcement Learning for Robot Controlcited by 4

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  1. 2025Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
  2. 2026Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
  3. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  4. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principles
  5. 2025RAPTOR: A Foundation Policy for Quadrotor Control
  6. 2025MAGNNET: Multi-Agent Graph Neural Network-based Efficient Task Allocation for Autonomous Vehicles with Deep Reinforcement Learning
  7. 2024Implementing TD3 to train a Neural Network to fly a Quadcopter through an FPV Gate
  8. 2026Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control