2 code implementations • 29 Oct 2022 • 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.
2 code implementations • 29 Oct 2022 • Resul Dagdanov, Feyza Eksen, Halil Durmus, Ferhat Yurdakul, Nazim Kemal Ure
In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios on these infraction areas to train an RL agent for fixing the shortcomings of the IL approach.
no code implementations • 14 Mar 2021 • Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural, Ferhat Yurdakul, Melih Dal, Nazim Kemal Ure
The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications.