  Generating competitive strategies and performing continuous motion planning
simultaneously in an adversarial setting is a challenging problem. In addition,
understanding the intent of other agents is crucial to deploying autonomous
systems in adversarial multi-agent environments. Existing approaches either
discretize agent action by grouping similar control inputs, sacrificing
performance in motion planning, or plan in uninterpretable latent spaces,
producing hard-to-understand agent behaviors. Furthermore, the most popular
policy optimization frameworks do not recognize the long-term effect of actions
and become myopic. This paper proposes an agent action discretization method
via abstraction that provides clear intentions of agent actions, an efficient
offline pipeline of agent population synthesis, and a planning strategy using
counterfactual regret minimization with function approximation. Finally, we
experimentally validate our findings on scaled autonomous vehicles in a
head-to-head racing setting. We demonstrate that using the proposed framework
significantly improves learning, improves the win rate against different
opponents, and the improvements can be transferred to unseen opponents in an
unseen environment.
