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Deep reinforcement learning with implicit imitation for lane-free autonomous driving

Chrysomallis Iason, Troullinos Dimitrios, Chalkiadakis Georgios, Papamichail Ioannis, Papageorgiou Markos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/10F634AF-6326-49E0-BD3F-78D66823D370-
Αναγνωριστικόhttps://ebooks.iospress.nl/doi/10.3233/FAIA230304-
Αναγνωριστικόhttps://doi.org/10.3233/FAIA230304-
Γλώσσαen-
Μέγεθος8 pagesen
ΤίτλοςDeep reinforcement learning with implicit imitation for lane-free autonomous drivingen
ΔημιουργόςChrysomallis Iasonen
ΔημιουργόςΧρυσομαλλης Ιασωνel
ΔημιουργόςTroullinos Dimitriosen
ΔημιουργόςΤρουλλινος Δημητριοςel
ΔημιουργόςChalkiadakis Georgiosen
ΔημιουργόςΧαλκιαδακης Γεωργιοςel
ΔημιουργόςPapamichail Ioannisen
ΔημιουργόςΠαπαμιχαηλ Ιωαννηςel
ΔημιουργόςPapageorgiou Markosen
ΔημιουργόςΠαπαγεωργιου Μαρκοςel
ΕκδότηςIOS Pressen
ΠεριγραφήThe research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ ERC Grant Agreement n. [833915], project TrafficFluid.en
ΠερίληψηImplicit imitation assumes that learning agents observe only the state transitions of an agent they use as a mentor, and try to recreate them based on their own abilities and knowledge of their environment. In this paper, we put forward a deep implicit imitation Q-network (DIIQN) model, which incorporates ideas from three well-known Deep Q-Network (DQN) variants. As such, we enable a novel implicit imitation method for online, model-free deep reinforcement learning. Our thorough experimentation in the complex environment of the emerging lane-free traffic paradigm, verifies the benefits of our approach. Specifically, we show that deep implicit imitation RL dramatically accelerates the learning process when compared to a “vanilla” DQN method; and, unlike explicit imitation reinforcement learning, it is able to outperform mentor performance without resorting to additional information, such as the mentor’s actions.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by-nc-nd/4.0/en
Ημερομηνία2024-06-28-
Ημερομηνία Δημοσίευσης2023-
Θεματική ΚατηγορίαDeep reinforcement learningen
Θεματική ΚατηγορίαImplicit imitationen
Θεματική ΚατηγορίαLane-free trafficen
Θεματική ΚατηγορίαAutonomous drivingen
Βιβλιογραφική ΑναφοράI. Chrysomallis, D. Troullinos, G. Chalkiadakis, I. Papamichail and M. Papageorgiou, “Deep reinforcement learning with implicit imitation for lane-free autonomous driving,” in ECAI 2023 - Proc. of the 26th European Conference on Artificial Intelligence, vol. 372, Frontiers in Artificial Intelligence and Applications, K. Gal, A. Nowé, G. J. Nalepa, R. Fairstein, R. Rădulescu, Eds., Amsterdam, The Netherlands: IOS Press, 2023, pp. 461-468, doi: 10.3233/faia230304.en

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