Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
This work addresses the problem of modeling realistic human driving behaviors in multi-agent systems for traffic simulation, representing an incremental improvement by enhancing existing imitation learning frameworks.
The paper tackles the challenge of capturing emergent traffic behaviors in multi-agent imitation learning by proposing Reward Augmented Imitation Learning (RAIL), which integrates reward augmentation to incorporate prior knowledge and preserves convergence guarantees, demonstrating improved performance over traditional methods in driving scenarios.
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers. However, it is challenging to capture emergent traffic behaviors that are observed in real-world datasets. Such behaviors arise due to the many local interactions between agents that are not commonly accounted for in imitation learning. This paper proposes Reward Augmented Imitation Learning (RAIL), which integrates reward augmentation into the multi-agent imitation learning framework and allows the designer to specify prior knowledge in a principled fashion. We prove that convergence guarantees for the imitation learning process are preserved under the application of reward augmentation. This method is validated in a driving scenario, where an entire traffic scene is controlled by driving policies learned using our proposed algorithm. Further, we demonstrate improved performance in comparison to traditional imitation learning algorithms both in terms of the local actions of a single agent and the behavior of emergent properties in complex, multi-agent settings.