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Autor:
Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov, Mira Ekim Vural, Ferhat Yurdakul, Melih Dal, Nazim Kemal Ure
Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a33d2a69ee23e50048e87fa7b5e28732
http://arxiv.org/abs/2103.07903
http://arxiv.org/abs/2103.07903
Publikováno v:
2020 IEEE Intelligent Vehicles Symposium (IV).
Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded successful results in computing lane changing policies that strike a