PREDICTION MODEL WITH VARIABLE TIME STEPS

Techniques for implementing a machine learned model to determine variable step sizes for searching a decision tree are described herein. For example, the techniques may include a computing device implementing a tree search algorithm to search the decision tree comprising nodes representing different possible object actions and/or vehicle actions at a future time. A search of the decision tree can be performed using multiple steps associated with different time periods to output a vehicle trajectory for the vehicle. The vehicle trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.