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.

翻译：强化学习（RL）在四旋翼飞行器控制中已展现出卓越成效，能够在单任务场景中训练出达到甚至超越人类冠军水平的专用策略。然而，这些专用策略在面对新任务时往往表现不佳，需要从零开始重新训练整个策略。为克服这一局限，本文提出一种专为四旋翼飞行器控制设计的新型多任务强化学习（MTRL）框架，该框架利用平台共享的物理动力学特性来提升样本效率与任务性能。通过采用多评论家架构与共享任务编码器，我们的框架实现了跨任务知识迁移，使单一策略能够执行包括高速稳定控制、速度跟踪与自主竞速在内的多种飞行动作。在仿真环境与真实场景中验证的实验结果表明，我们的框架在样本效率与整体任务性能方面均优于基线方法。