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 https://github.com/AutonomousDrivingExaminer/mats-gym.

翻译：多样化复杂训练场景的自动生成已成为许多复杂学习任务中的关键要素。尤其在自动驾驶等实际应用领域，自动课程生成被视为获得鲁棒且通用策略的重要手段。然而，在更复杂的仿真环境中，构建包含多个异构智能体的交通场景通常被认为是一项繁琐且耗时的任务。在本研究中，我们提出MATS-Gym——一个基于CARLA高保真驾驶仿真器的多智能体交通场景训练框架。MATS-Gym是一种用于自动驾驶的多智能体训练框架，通过部分场景规范生成包含可变数量智能体的交通场景。本文统一了现有多种交通场景描述方法，将其整合为单一训练框架，并展示了如何与环境无监督设计技术相结合，实现自适应自动课程生成的自动化。相关代码已开源至https://github.com/AutonomousDrivingExaminer/mats-gym。