The automated generation of diverse and complex training scenarios has been
an important ingredient in many complex learning tasks. Especially in
real-world application domains, such as autonomous driving, auto-curriculum
generation is considered vital for obtaining robust and general policies.
However, crafting traffic scenarios with multiple, heterogeneous agents is
typically considered as a tedious and time-consuming task, especially in more
complex simulation environments. In our work, we introduce MATS-Gym, a
Multi-Agent Traffic Scenario framework to train agents in CARLA, a
high-fidelity driving simulator. MATS-Gym is a multi-agent training framework
for autonomous driving that uses partial scenario specifications to generate
traffic scenarios with variable numbers of agents. This paper unifies various
existing approaches to traffic scenario description into a single training
framework and demonstrates how it can be integrated with techniques from
unsupervised environment design to automate the generation of adaptive
auto-curricula. The code is available at
this https URL

自动化生成多样且复杂的训练场景对于许多复杂的学习任务至关重要，本论文介绍了 MAT-Gym，这是一个用于 CARLA 高保真驾驶模拟器的多智能体交通场景训练框架，通过使用部分场景描述来生成具有不同数量智能体的交通场景，并演示了如何将其与无监督环境设计技术相结合以自动化生成自适应的自动课程。

多智能体自动驾驶的基于场景的课程生成

Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving

Persistent topological properties of an image serve as an additional
descriptor providing an insight that might not be discovered by traditional
neural networks. The existing research in this area focuses primarily on
efficiently integrating topological properties of the data in the learning
process in order to enhance the performance. However, there is no existing
study to demonstrate all possible scenarios where introducing topological
properties can boost or harm the performance. This paper performs a detailed
analysis of the effectiveness of topological properties for image
classification in various training scenarios, defined by: the number of
training samples, the complexity of the training data and the complexity of the
backbone network. We identify the scenarios that benefit the most from
topological features, e.g., training simple networks on small datasets.
Additionally, we discuss the problem of topological consistency of the datasets
which is one of the major bottlenecks for using topological features for
classification. We further demonstrate how the topological inconsistency can
harm the performance for certain scenarios.

本文旨在分析拓扑特征在不同训练场景下对图像分类的有效性，并讨论数据集的拓扑一致性问题对于分类性能的影响。