Training machine learning model(s), in simulation, for use in controlling autonomous vehicle(s)

Abstract

Systems and methods for training a machine learning ('ML') model for use in controlling an autonomous vehicle (“AV”) are described herein. Implementations can obtain an initial state instance from driving of a vehicle, obtain ground truth label(s) for subsequent state instance(s) that each indicate a corresponding action of the vehicle for a corresponding time instance, perform, for a given time interval, a simulated episode, of locomotion of a simulated AV, generate, for each of a plurality of time instances of the given time interval, subsequent simulated state instance(s) that differ from the subsequent state instance(s), determine, using the ML model, and for each of the time instances, a predicted simulated action of the simulated AV based on the subsequent simulated operation instance(s), generate loss(es) based on the predicted simulated actions and the ground truth labels, and update the ML model based on the loss(es).