Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving
Axel Brunnbauer Luigi Berducci Peter Priller Dejan Nickovic Radu Grosu
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.