  Interactions between road agents present a significant challenge in
trajectory prediction, especially in cases involving multiple agents. Because
existing diversity-aware predictors do not account for the interactive nature
of multi-agent predictions, they may miss these important interaction outcomes.
In this paper, we propose NashFormer, a framework for trajectory prediction
that leverages game-theoretic inverse reinforcement learning to improve
coverage of multi-modal predictions. We use a training-time game-theoretic
analysis as an auxiliary loss resulting in improved coverage and accuracy
without presuming a taxonomy of actions for the agents. We demonstrate our
approach on the interactive split of the Waymo Open Motion Dataset, including
four subsets involving scenarios with high interaction complexity. Experiment
results show that our predictor produces accurate predictions while covering
$33\%$ more potential interactions versus a baseline model.
