# Multi-Task Reinforcement Learning for Quadrotors

arXiv: https://arxiv.org/abs/2412.12442 · HTML: https://mlsanity.com/paper/2412.12442/

## Key facts

- Cited by 20 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 26 of its 29 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## 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, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Conflict-Averse Gradient Descent for Multi-task Learning](https://mlsanity.com/paper/2110.14048/) (2021)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://mlsanity.com/paper/2006.05990/) (2020)
- [Multi-Task Reinforcement Learning with Soft Modularization](https://mlsanity.com/paper/2003.13661/) (2020)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention](https://mlsanity.com/paper/2104.11203/) (2021)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning](https://mlsanity.com/paper/1901.03737/) (2019)
- [Sharing Knowledge in Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/2401.09561/) (2024)
- [Lifelong Robotic Reinforcement Learning by Retaining Experiences](https://mlsanity.com/paper/2109.09180/) (2021)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight](https://mlsanity.com/paper/2403.12203/) (2024)
- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [Autonomous Power Line Inspection with Drones via Perception-Aware MPC](https://mlsanity.com/paper/2304.00959/) (2023)
- [Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight](https://mlsanity.com/paper/2309.09865/) (2023)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2309.13285/) (2023)
- [Environment as Policy: Learning to Race in Unseen Tracks](https://mlsanity.com/paper/2410.22308/) (2024)
- [LIMT: Language-Informed Multi-Task Visual World Models](https://mlsanity.com/paper/2407.13466/) (2024)

## Built on by (most cited first, 20 of 20)

- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (2025)
- [Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight](https://mlsanity.com/paper/2501.14377/) (2025)
- [Accelerating Model-Based Reinforcement Learning with State-Space World Models](https://mlsanity.com/paper/2502.20168/) (2025)
- [RAPTOR: A Foundation Policy for Quadrotor Control](https://mlsanity.com/paper/2509.11481/) (2025)
- [TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning](https://mlsanity.com/paper/2503.01125/) (2025)
- [CARoL: Context-aware Adaptation for Robot Learning](https://mlsanity.com/paper/2506.07006/) (2025)
- [Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles](https://mlsanity.com/paper/2602.24030/) (2026)
- [Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments](https://mlsanity.com/paper/2603.07578/) (2026)
- [Maintaining Plasticity in Reinforcement Learning: A Cost-Aware Framework for Aerial Robot Control in Non-stationary Environments](https://mlsanity.com/paper/2503.00282/) (2025)
- [Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy](https://mlsanity.com/paper/2503.08259/) (2025)
- [Learning Acrobatic Flight from Preferences](https://mlsanity.com/paper/2508.18817/) (2025)
- [Leveraging Temporally Extended Behavior Sharing for Multi-task Reinforcement Learning](https://mlsanity.com/paper/2509.20766/) (2025)
- [Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing](https://mlsanity.com/paper/2510.22570/) (2025)
- [Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetries](https://mlsanity.com/paper/2602.10997/) (2026)
- [Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments](https://mlsanity.com/paper/2605.19202/) (2026)
- [Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning](https://mlsanity.com/paper/2606.25700/) (2026)
- [Bridging Performance and Generalization in Reinforcement Learning for Agile Flight](https://mlsanity.com/paper/2606.27348/) (2026)
- [Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics](https://mlsanity.com/paper/2607.25985/) (2026)
- [Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret](https://mlsanity.com/paper/2607.26370/) (2026)
- [Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection](https://mlsanity.com/paper/2610.10601/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has shown great effectiveness in quadrotor control, enabling specialized policies to develop even human-champion-level performance in single-task scenarios. However, these specialized policies often struggle with novel tasks, requiring a complete retraining of the policy from scratch. To address this limitation, this paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control, leveraging the shared physical dynamics of the platform to enhance sample efficiency and task performance. By employing a multi-critic architecture and shared task encoders, our framework facilitates knowledge transfer across tasks, enabling a single policy to execute diverse maneuvers, including high-speed stabilization, velocity tracking, and autonomous racing. Our experimental results, validated both in simulation and real-world scenarios, demonstrate that our framework outperforms baseline approaches in terms of sample efficiency and overall task performance.