Autonomous Car Racing in Simulation Environment Using Deep Reinforcement Learning

Kivanc Guckiran, Bulent Bolat

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

18 Atıf (Scopus)

Özet

Self-Driving Cars are, currently a hot topic throughout the globe thanks to the advancements in Deep Learning techniques on computer vision problems. Since driving simulations are fairly important before real life autonomous implementations, there are multiple driving-racing simulations for testing purposes. The Open Racing Car Simulation (TORCS) is a highly portable open source car racing-self-driving-simulation. While it can be used as a game in which human players compete with scripted agents, TORCS provides observation and action API to develop an artificial intelligence agent. This study explores near-optimal Deep Reinforcement Learning agents for TORCS environment using Soft Actor-Critic and Rainbow DQN algorithms, exploration and generalization techniques.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781728128689
DOI'lar
Yayın durumuYayınlandı - Eki 2019
Harici olarak yayınlandıEvet
Etkinlik2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 - Izmir, Turkey
Süre: 31 Eki 20192 Kas 2019

Yayın serisi

AdıProceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019

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???event.eventtypes.event.conference???2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019
Ülke/BölgeTurkey
ŞehirIzmir
Periyot31/10/192/11/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

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