Automobile racing provides a unique and challenging environment for studying competitive multi-agent behavior. In creating autonomous racing agents, one consideration is the effect that modeling one’s opponents has on finding high-performance policies. In this paper, we study the overall effectiveness of opponent modeling in the context of autonomous racing, as well as the value of different information about one’s opponents. We propose a new approach for learning salient characteristics of one’s opponent: Learn Thy Enemy (LTE), an algorithmic framework that combines reinforcement learning with self-supervised learning about one’s opponents. We evaluate LTE against multiple baselines in a CARLA-based simulation of an actual major racetrack. The results demonstrate that LTE substantially outperforms baselines, showing LTE’s effectiveness in extracting relevant opponent information automatically during interactions with the aim of better accomplishing the task. READ MORE