Pedestrian steering algorithms range from completely procedural to entirely data-driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever-increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine-learned policies based on the behavior of a procedural steering algorithm through the decomposition of the space of possible steering scenarios into steering contexts. The resulting algorithm scales well in the number of contexts, the use of new data sets to create new policies, and in the number of controlled agents as the policies become a simple evaluation of the rules asserted by the machine-learning process. We also explore the use of synthetic data from an “oracle algorithm” that serves as an as-needed source of samples, which can be stochastically polled for effective coverage. We observe that our approach produces pedestrian steering similar to that of the oracle steering algorithm, but with a significant performance boost. Runtime was reduced from hours under the oracle algorithm with 10 agents to on the order of 10 frames per second (FPS) with 3000 agents. We also analyze the nature of collisions in such a framework with no explicit collision avoidance.