Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

• URL: http://arxiv.org/abs/2210.16575v3
• Date: Sun, 9 Jul 2023 16:42:37 GMT
• Title: Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
• Authors: Resul Dagdanov, Halil Durmus, Nazim Kemal Ure
• Abstract summary: We propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents. Our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://github.com/data-and-decision-lab/self-improving-RL.

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