Reinforcement learning~(RL) is a versatile framework for learning to solve
complex real-world tasks. However, influences on the learning performance of RL
algorithms are often poorly understood in practice. We discuss different
analysis techniques and assess their effectiveness for investigating the impact
of action representations in RL. Our experiments demonstrate that the action
representation can significantly influence the learning performance on popular
RL benchmark tasks. The analysis results indicate that some of the performance
differences can be attributed to changes in the complexity of the optimization
landscape. Finally, we discuss open challenges of analysis techniques for RL
algorithms.

强化学习是一种用于解决复杂实际问题的多功能学习框架，本研究讨论了不同的分析技术，并评估它们对于研究强化学习中行为表示对学习性能的影响的有效性。实验证明行为表示对流行强化学习基准任务的学习性能有显著影响，分析结果指出性能差异的一部分可以归因于优化整体的复杂度变化。最后，我们讨论了强化学习算法分析技术的挑战。

调查策略梯度算法中行动表示的影响

Investigating the Impact of Action Representations in Policy Gradient  Algorithms

Uniform stability of a learning algorithm is a classical notion of
algorithmic stability introduced to derive high-probability bounds on the
generalization error (Bousquet and Elisseeff, 2002). Specifically, for a loss
function with range bounded in $[0,1]$, the generalization error of a
$\gamma$-uniformly stable learning algorithm on $n$ samples is known to be
within $O((\gamma +1/n) \sqrt{n \log(1/\delta)})$ of the empirical error with
probability at least $1-\delta$. Unfortunately, this bound does not lead to
meaningful generalization bounds in many common settings where $\gamma \geq
1/\sqrt{n}$. At the same time the bound is known to be tight only when $\gamma
= O(1/n)$.
We substantially improve generalization bounds for uniformly stable
algorithms without making any additional assumptions. First, we show that the
bound in this setting is $O(\sqrt{(\gamma + 1/n) \log(1/\delta)})$ with
probability at least $1-\delta$. In addition, we prove a tight bound of
$O(\gamma^2 + 1/n)$ on the second moment of the estimation error. The best
previous bound on the second moment is $O(\gamma + 1/n)$. Our proofs are based
on new analysis techniques and our results imply substantially stronger
generalization guarantees for several well-studied algorithms.

提出了一种新的分析技术，证明了一些熟知算法具有更强的泛化保证，并改善了这个算法稳定性的相关细节。

基于一致稳定算法的泛化界限

Generalization Bounds for Uniformly Stable Algorithms

Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques.

本文旨在开发一个价值驱动的框架来分类现有的预测过程监控工作，通过系统地识别、分类和分析现有的预测过程监控方法，开发一个价值驱动的框架，以帮助组织在预测过程监控领域中找到价值并利用这些分析技术的机会。