Meta-reinforcement learning for active visual tracking about space non-cooperative object

This paper addresses the challenge of active tracking of space non-cooperative targets, a critical task in various aerospace applications. Traditional active tracking algorithms often require extensive data and suffer from limited generalization ability, making them inefficient for tracking targets with diverse characteristics. To overcome these limitations, we propose an end-to-end active target tracking method named Meta-Reinforcement Learning based Active Visual Tracking (MRLAVT). This approach integrates meta-reinforcement learning, enabling the system to quickly adapt to new tasks by leveraging experiences from previous tasks. By employing convolutional neural networks to extract information from images and generate corresponding actions, MRLAVT demonstrates strong adaptability and robustness in tracking targets with varying characteristics. Experimental results confirm the effectiveness of our proposed algorithm, showcasing superior performance in scenarios involving both few adaptations and non-adaptation. Overall, MRLAVT significantly reduces the complexity of system integration while achieving high-quality tracking results.