Multi-Agent Imitation Learning for Driving Simulation

Understand

Simulation is an appealing option for validating the safety of autonomous vehicles.

• Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models.
• These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios.
• We argue these difficulties arise because observations at training and test time are sampled from different distributions.