Reinforcement learning (RL) algorithms interact with their environment in a
trial-and-error fashion. Such interactions can be expensive, inefficient, and
timely when learning on a physical system rather than in a simulation. This
work develops new runtime verification techniques to predict when the learning
phase has not met or will not meet qualitative and timely expectations. This
paper presents three verification properties concerning the quality and
timeliness of learning in RL algorithms. With each property, we propose design
steps for monitoring and assessing the properties during the system's
operation.

该研究提出了针对强化学习算法的运行时验证技术，用于预测学习阶段是否满足质量和及时性的期望，并提出了三个验证属性和相应的监测与评估步骤。

强化学习算法学习属性的运行时验证

Runtime Verification of Learning Properties for Reinforcement Learning  Algorithms

Long-Term tracking is a hot topic in Computer Vision. In this context,
competitive models are presented every year, showing a constant growth rate in
performances, mainly measured in standardized protocols as Visual Object
Tracking (VOT) and Object Tracking Benchmark (OTB). Fusion-trackers strategy
has been applied over last few years for overcoming the known re-detection
problem, turning out to be an important breakthrough. Following this approach,
this work aims to generalize the fusion concept to an arbitrary number of
trackers used as baseline trackers in the pipeline, leveraging a learning phase
to better understand how outcomes correlate with each other, even when no
target is present. A model and data independence conjecture will be evidenced
in the manuscript, yielding a recall of 0.738 on LTB-50 dataset when learning
from VOT-LT2022, and 0.619 by reversing the two datasets. In both cases,
results are strongly competitive with state-of-the-art and recall turns out to
be the first on the podium.

该论文总结了长期跟踪中的关键问题，提出了基于融合追踪器策略的学习方法，并通过实验证明了模型与数据的独立性，并在竞争中取得了领先地位。