Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving

arXiv:2403.17805v1 Announce Type: cross
Abstract: 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 …

agent arxiv autonomous autonomous driving cs.lg cs.ma cs.ro curriculum driving multi-agent type