Past, Present and Future are considered to be three temporal and logical
concepts which are well defined by human beings for their existence and growth.
We, as human beings, have the privilege of using our intelligence to mentally
execute an activity before physical occurrence of the same in the real world.
Knowledge of the past, aplomb of present and visualisation for the future
correspond to three concepts such as look-back, look-at and look-ahead
respectively in real life as well as in diversified domains of computing.
Look-Ahead(LA) deals with the future prediction of information and processing
of input to produce the output in advance. In this article, our main objective
is to learn, understand and explore the concept of LA and design novel models
as solution for real world problems. We present three well known algorithmic
frameworks used in practice based on availability of input information such as
offline, online and semi-online. We introduce interesting real life
applications and well known computing problems where LA plays a significant
role for making a process, system or algorithm efficient. We define new types
of LA and propose a taxonomy for LA based on literature review for designing
novel LA models in future. Using the concept of LA, We identify and present
many interesting and non-trivial research challenges as future potential
research directions. Intuitively, we observe that LA can be used as a powerful
tool and framework for future researchers in design of efficient computational
models and algorithms for solving non-trivial and challenging optimization
problems.

通过对未来信息的预测和处理提前生成输出，Look-Ahead（LA）在实际中具有重要作用。本文介绍了三种实践中使用的算法框架，包括线下、线上和半在线方法，并提出了基于文献综述的 LA 分类。此外，本文还识别和提出了未来潜在研究方向中的许多有趣且非平凡的研究挑战。

关于现实生活和计算中的向前看的注释

A Note On Lookahead In Real Life And Computing

This paper endeavors to augment the robustness of offline reinforcement
learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent
circumstance in real-world applications. We propose two algorithmic frameworks,
ROAM and ROOM, for robust off-policy evaluation (OPE) and offline policy
optimization (OPO), respectively. Central to our frameworks is the strategic
incorporation of the median-of-means method with offline RL, enabling
straightforward uncertainty estimation for the value function estimator. This
not only adheres to the principle of pessimism in OPO but also adeptly manages
heavy-tailed rewards. Theoretical results and extensive experiments demonstrate
that our two frameworks outperform existing methods on the logged dataset
exhibits heavy-tailed reward distributions.

这篇论文旨在增强离线强化学习在实际应用场景中具有重尾奖励的鲁棒性。我们提出了两个算法框架 ROAM 和 ROOM，分别用于鲁棒的离线策略评估和离线策略优化。这些框架的核心是将均值中位数方法与离线强化学习相结合，从而能够直接估计值函数估计器的不确定性。理论结果和广泛的实验证明，我们的两个框架在具有重尾奖励分布的日志数据集上优于现有方法。