Multi-Task Reinforcement Learning for Quadrotors

• URL: http://arxiv.org/abs/2412.12442v1
• Date: Tue, 17 Dec 2024 01:10:18 GMT
• Title: Multi-Task Reinforcement Learning for Quadrotors
• Authors: Jiaxu Xing, Ismail Geles, Yunlong Song, Elie Aljalbout, Davide Scaramuzza,
• Abstract summary: This paper presents a novel multi-task reinforcement learning (MTRL) framework tailored for quadrotor control. 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.
• Score: 18.71563817810032
• License:
• Abstract: 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.

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