Simulation is an essential tool to develop and benchmark autonomous vehicle
planning software in a safe and cost-effective manner. However, realistic
simulation requires accurate modeling of nuanced and complex multi-agent
interactive behaviors. To address these challenges, we introduce Waymax, a new
data-driven simulator for autonomous driving in multi-agent scenes, designed
for large-scale simulation and testing. Waymax uses publicly-released,
real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize or
play back a diverse set of multi-agent simulated scenarios. It runs entirely on
hardware accelerators such as TPUs/GPUs and supports in-graph simulation for
training, making it suitable for modern large-scale, distributed machine
learning workflows. To support online training and evaluation, Waymax includes
several learned and hard-coded behavior models that allow for realistic
interaction within simulation. To supplement Waymax, we benchmark a suite of
popular imitation and reinforcement learning algorithms with ablation studies
on different design decisions, where we highlight the effectiveness of routes
as guidance for planning agents and the ability of RL to overfit against
simulated agents.

Waymax 是一个新的基于数据驱动的自动驾驶多代理场景模拟器，它使用真实世界的驾驶数据初始化和回放多样的模拟场景，并支持训练中的图内模拟，适用于现代的大规模分布式机器学习工作流程。它还包括多个学习和硬编码的行为模型，以支持在模拟环境中进行真实交互。通过对不同设计决策进行消融研究，我们还对一套常用的模仿学习和强化学习算法进行了基准测试，强调了路线规划对规划代理的有效性以及强化学习对模拟代理的过拟合能力。

Waymax: 大规模自动驾驶研究的加速、数据驱动模拟器

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous  Driving Research

A substantial part of fighter pilot training is simulation-based and involves
computer-generated forces controlled by predefined behavior models. The
behavior models are typically manually created by eliciting knowledge from
experienced pilots, which is a time-consuming process. Despite the work put in,
the behavior models are often unsatisfactory due to their predictable nature
and lack of adaptivity, forcing instructors to spend time manually monitoring
and controlling them. Reinforcement and imitation learning pose as alternatives
to handcrafted models. This paper presents the Learning Environment for the Air
Domain (LEAD), a system for creating and integrating intelligent air combat
behavior in military simulations. By incorporating the popular programming
library and interface Gymnasium, LEAD allows users to apply readily available
machine learning algorithms. Additionally, LEAD can communicate with
third-party simulation software through distributed simulation protocols, which
allows behavior models to be learned and employed using simulation systems of
different fidelities.

LEAD 是一个基于机器学习来创建和整合智能空中战斗行为的系统，它能够与不同精度的模拟系统进行通信和协作，有效减少军事模拟训练中手工命令模式的时间成本。

空域学习环境（LEAD）

Learning Environment for the Air Domain (LEAD)

Click-through rate (CTR) estimation plays as a core function module in
various personalized online services, including online advertising, recommender
systems, and web search etc. From 2015, the success of deep learning started to
benefit CTR estimation performance and now deep CTR models have been widely
applied in many industrial platforms. In this survey, we provide a
comprehensive review of deep learning models for CTR estimation tasks. First,
we take a review of the transfer from shallow to deep CTR models and explain
why going deep is a necessary trend of development. Second, we concentrate on
explicit feature interaction learning modules of deep CTR models. Then, as an
important perspective on large platforms with abundant user histories, deep
behavior models are discussed. Moreover, the recently emerged automated methods
for deep CTR architecture design are presented. Finally, we summarize the
survey and discuss the future prospects of this field.

本文综述了 CTR 估算任务中使用的深度学习模型，包括从浅层到深度 CTR 模型的转换，深度 CTR 模型的显式特征交互学习模块，用户行为模型，以及深度 CTR 架构设计的自动化方法。