  Traffic simulators are important tools in autonomous driving development.
While continuous progress has been made to provide developers more options for
modeling various traffic participants, tuning these models to increase their
behavioral diversity while maintaining quality is often very challenging. This
paper introduces an easily-tunable policy generation algorithm for autonomous
driving agents. The proposed algorithm balances diversity and driving skills by
leveraging the representation and exploration abilities of deep reinforcement
learning via a distinct policy set selector. Moreover, we present an algorithm
utilizing intrinsic rewards to widen behavioral differences in the training. To
provide quantitative assessments, we develop two trajectory-based evaluation
metrics which measure the differences among policies and behavioral coverage.
We experimentally show the effectiveness of our methods on several challenging
intersection scenes.
