Humans often acquire new skills through observation and imitation. For
robotic agents, learning from the plethora of unlabeled video demonstration
data available on the Internet necessitates imitating the expert without access
to its action, presenting a challenge known as Imitation Learning from
Observations (ILfO). A common approach to tackle ILfO problems is to convert
them into inverse reinforcement learning problems, utilizing a proxy reward
computed from the agent's and the expert's observations. Nonetheless, we
identify that tasks characterized by a progress dependency property pose
significant challenges for such approaches; in these tasks, the agent needs to
initially learn the expert's preceding behaviors before mastering the
subsequent ones. Our investigation reveals that the main cause is that the
reward signals assigned to later steps hinder the learning of initial
behaviors. To address this challenge, we present a novel ILfO framework that
enables the agent to master earlier behaviors before advancing to later ones.
We introduce an Automatic Discount Scheduling (ADS) mechanism that adaptively
alters the discount factor in reinforcement learning during the training phase,
prioritizing earlier rewards initially and gradually engaging later rewards
only when the earlier behaviors have been mastered. Our experiments, conducted
on nine Meta-World tasks, demonstrate that our method significantly outperforms
state-of-the-art methods across all tasks, including those that are unsolvable
by them.

通过自动折扣调度的机制适应性地改变强化学习的折扣系数，我们提出了一种新颖的观察学习框架，使得代理能够在掌握较早行为之后再逐步转向后续行为，并通过在九个 Meta-World 任务上的实验证明该方法在所有任务中明显优于现有方法。

基于观察的模仿学习与自动折扣调度

Imitation Learning from Observation with Automatic Discount Scheduling

Active automata learning algorithms cannot easily handle conflict in the
observation data (different outputs observed for the same inputs). This
inherent inability to recover after a conflict impairs their effective
applicability in scenarios where noise is present or the system under learning
is mutating. We propose the Conflict-Aware Active Automata Learning (C3AL)
framework to enable handling conflicting information during the learning
process. The core idea is to consider the so-called observation tree as a
first-class citizen in the learning process. Though this idea is explored in
recent work, we take it to its full effect by enabling its use with any
existing learner and minimizing the number of tests performed on the system
under learning, specially in the face of conflicts. We evaluate C3AL in a large
set of benchmarks, covering over 30 different realistic targets, and over
18,000 different scenarios. The results of the evaluation show that C3AL is a
suitable alternative framework for closed-box learning that can better handle
noise and mutations.

Conflicts in observation data pose a challenge for active automata learning algorithms, but the Conflict-Aware Active Automata Learning (C3AL) framework addresses this by considering the observation tree as a first-class citizen during the learning process, enabling better handling of noise and mutations.

冲突感知主动自动机学习

Conflict-Aware Active Automata Learning

Classroom discourse is a core medium of instruction -- analyzing it can
provide a window into teaching and learning as well as driving the development
of new tools for improving instruction. We introduce the largest dataset of
mathematics classroom transcripts available to researchers, and demonstrate how
this data can help improve instruction. The dataset consists of 1,660 45-60
minute long 4th and 5th grade elementary mathematics observations collected by
the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The
anonymized transcripts represent data from 317 teachers across 4 school
districts that serve largely historically marginalized students. The
transcripts come with rich metadata, including turn-level annotations for
dialogic discourse moves, classroom observation scores, demographic
information, survey responses and student test scores. We demonstrate that our
natural language processing model, trained on our turn-level annotations, can
learn to identify dialogic discourse moves and these moves are correlated with
better classroom observation scores and learning outcomes. This dataset opens
up several possibilities for researchers, educators and policymakers to learn
about and improve K-12 instruction. The data and its terms of use can be
accessed here: this https URL

通过分析教室话语可窥探教学及学习背后的因素，本文介绍了迄今规模最大的数学类教室话语记录数据库，通过自然语言处理学习对话交互语言并探究其与优质教学评估及学生学习成果的关系，为研究者、教育者和政策制定者提供了参考价值。

NCTE 记录：小学数学课堂记录数据集

The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts

Identifying causal relationships from observation data is difficult, in large
part, due to the presence of hidden common causes. In some cases, where just
the right patterns of conditional independence and dependence lie in the
data---for example, Y-structures---it is possible to identify cause and effect.
In other cases, the analyst deliberately makes an uncertain assumption that
hidden common causes are absent, and infers putative causal relationships to be
tested in a randomized trial. Here, we consider a third approach, where there
are sufficient clues in the data such that hidden common causes can be
inferred.

本文讨论探讨了一种新的方法，可以通过观察数据中隐藏的共同原因进行推断和检验因果关系，而不是直接基于假设。