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 dil | İngilizce |
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Ana bilgisayar yayını başlığı | Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Elektronik) | 9781728128689 |
DOI'lar | |
Yayın durumu | Yayınlandı - Eki 2019 |
Harici olarak yayınlandı | Evet |
Etkinlik | 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 - Izmir, Turkey Süre: 31 Eki 2019 → 2 Kas 2019 |
Yayın serisi
Adı | Proceedings - 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 |
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Ülke/Bölge | Turkey |
Şehir | Izmir |
Periyot | 31/10/19 → 2/11/19 |
Bibliyografik not
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