Human in the loop learning in Autonomous Driving
|Human in the loop learning in Autonomous Driving
|Alkis Sygkounas <firstname.lastname@example.org>
|2023-12-12 – 2024-04-01
As autonomous driving technology advances, ensuring the safety and efficiency of these systems remains paramount.In this paper we introduce an online approach of combining double dueling deep Q-learning (DDDQN) with human-in-the-loop (HITL) learning to enhance the performance of autonomous driving systems. This combination harnesses the computational capabilities of deep reinforcement learning while leveraging the real-time insights of human feedback.The results suggest that, integrating human feedback leads to a notable performance enhancement in the DDDQN algorithm, surpassing the efficiency and outcomes of baseline models trained exclusively with reinforcement learning. Perfmorm x\% better in convergance, robustness and transfer learning or scalability.
For that we will use microsoft AirSim, tensorflwo gpu and other minor (needed) libraries.