Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
Resul Dagdanov,Halil Durmus,Nazim Kemal Ure,Resul Dagdanov,Halil Durmus,Nazim Kemal Ure
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 ...
