Self-Training Autonomous Driving System Using An Advantage-Actor-Critic Model


Student Name: Elise McEllhiney
Defense Date:
Location: Eaton Hall, Room 2001B
Chair: Victor Frost

Prasad Kulkarni

Bo Luo

Abstract:

We describe an autonomous driving system that uses reinforcement learning to train a car to drive without the need for collecting training input from human drivers.  We achieve this by using the Advantage Actor Critic reinforcement system that trains the car based on continuously adapting the model to minimize the penalty received by the car.  This penalty is determined if the car intersected the borders of the track on which it is driving.  We show the resilience of the proposed autonomously trained system to noisy sensor inputs and variations in the shape of the track.

Degree: MS Project Defense (CoE)
Degree Type: MS Project Defense
Degree Field: Computer Engineering