Reinforcement learning on autonomous vehicles

Abstract

The present disclosure generally relates to methods and systems for controlling an autonomous vehicle. The vehicle may collect scenario information from one or more sensors mounted on a vehicle. The vehicle may determine a high-level option for a fixed time horizon based on the scenario information. The vehicle may apply a prediction algorithm to the high-level option to mask undesired low-level behaviors for completing the high-level option where a collision is predicted to occur. The vehicle may evaluate a restricted subspace of low-level behaviors using a reinforcement learning system. The vehicle may control the vehicle to perform the high-level option by executing a low-level behavior selected from the restricted subspace. The vehicle may adjust the reinforcement learning system by evaluating a metric of the executed low-level behavior.