一个可同时学会多种飞行任务的无人机强化学习框架。
Multi-Task Reinforcement Learning for Quadrotors
- 用共享编码器和多评判器结构，让无人机在多个任务间共享经验。
- 实测表明训练效率提升，单个策略能完成高速稳定、速度追踪等多样任务。
- 适合想快速部署多功能无人机系统的研发人员使用。
强化学习在四旋翼控制中表现出色，能在单一任务中达到人类顶尖水平。但专用策略难以应对新任务，需从头重新训练。本文提出一种面向四旋翼控制的多任务强化学习（MTRL）框架，利用平台共有的物理动力学特性，提升样本效率与任务性能。通过多评判器架构和共享任务编码器，实现跨任务知识迁移，使单一策略可执行高速稳定、速度跟踪及自主竞速等多种机动。实验结果在仿真和真实场景中均表明，该框架在样本效率和整体任务表现上优于基线方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。