The advent of compact, handheld devices has given us a pool of tracked
movement data that could be used to infer trends and patterns that can be made
to use. With this flooding of various trajectory data of animals, humans,
vehicles, etc., the idea of ANALYTiC originated, using active learning to infer
semantic annotations from the trajectories by learning from sets of labeled
data. This study explores the application of dimensionality reduction and
decision boundaries in combination with the already present active learning,
highlighting patterns and clusters in data. We test these features with three
different trajectory datasets with objective of exploiting the the already
labeled data and enhance their interpretability. Our experimental analysis
exemplifies the potential of these combined methodologies in improving the
efficiency and accuracy of trajectory labeling. This study serves as a
stepping-stone towards the broader integration of machine learning and visual
methods in context of movement data analysis.

使用主动学习和降维技术结合决策边界，提取轨迹数据中的模式和聚类特征，以提高轨迹标记的效率和准确性。

ANALYTiC：机器学习中决策边界和降维的理解

ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction  in Machine Learning

A popular framework for enforcing safe actions in Reinforcement Learning (RL)
is Constrained RL, where trajectory based constraints on expected cost (or
other cost measures) are employed to enforce safety and more importantly these
constraints are enforced while maximizing expected reward. Most recent
approaches for solving Constrained RL convert the trajectory based cost
constraint into a surrogate problem that can be solved using minor
modifications to RL methods. A key drawback with such approaches is an over or
underestimation of the cost constraint at each state. Therefore, we provide an
approach that does not modify the trajectory based cost constraint and instead
imitates ``good'' trajectories and avoids ``bad'' trajectories generated from
incrementally improving policies. We employ an oracle that utilizes a reward
threshold (which is varied with learning) and the overall cost constraint to
label trajectories as ``good'' or ``bad''. A key advantage of our approach is
that we are able to work from any starting policy or set of trajectories and
improve on it. In an exhaustive set of experiments, we demonstrate that our
approach is able to outperform top benchmark approaches for solving Constrained
RL problems, with respect to expected cost, CVaR cost, or even unknown cost
constraints.

通过模仿学习和轨迹标记的方法，解决强化学习中的约束问题，并在实验中展示了其优越性能。