Scenario-Based Curriculum Generation for Multi-Agent Driving

Abstract

The automated generation of diversified training scenarios has been an important ingredient in many complex learning tasks, especially in real-world application domains such as autonomous driving, where auto-curriculum generation is considered vital for obtaining robust and general policies. However, crafting traffic scenarios with multiple, heterogeneous agents is typically considered a tedious and time-consuming task, especially in more complex simulation environments. To this end, we introduce MATS-Gym, a multi-agent training framework for autonomous driving that uses partial-scenario specifications to generate traffic scenarios with a variable number of agents which are executed in CARLA, a high-fidelity driving simulator. MATS-Gym reconciles scenario execution engines, such as Scenic and ScenarioRunner, with established multi-agent training frameworks where the interaction between the environment and the agents is modeled as a partially observable stochastic game. Furthermore, we integrate MATSGym with techniques from unsupervised environment design to automate the generation of adaptive auto-curricula, which is the first application of such algorithms to the domain of autonomous driving. The code is available at https://github.com/AutonomousDrivingExaminer/mats-gym.