Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi

Dogay Kamar; Gamze Akyol; Alican Mertan; Arda Inceoglu

doi:10.1109/SIU49456.2020.9302358

Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi

Dogay Kamar
, Gamze Akyol
, Alican Mertan
, Arda Inceoglu

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

1 Atıf (Scopus)

Özet

In this study, reinforcement learning algorithms are compared in TORCS simulation environment. In this simulation environment, the goal is to finish the track as soon as possible by controlling the car. The agent decides actions by using highlevel observations from the environment. For this goal, two reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results are compared and analyzed. Since the action space is continuous, DDPG algorithm performed better as expected. However, we were able to show that DQN algorithm also gives comparable results.

Tercüme edilen katkı başlığı	Comparative Analysis of Reinforcement Learning Algorithms on TORCS Environment
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728172064
DOI'lar	https://doi.org/10.1109/SIU49456.2020.9302358
Yayın durumu	Yayınlandı - 5 Eki 2020
Etkinlik	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Süre: 5 Eki 2020 → 7 Eki 2020

Yayın serisi

Adı	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	28th Signal Processing and Communications Applications Conference, SIU 2020
Ülke/Bölge	Turkey
Şehir	Gaziantep
Periyot	5/10/20 → 7/10/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

Keywords

ddpg
double dqn.
dqn
reinforcement learning

Belgeye Erişim

10.1109/SIU49456.2020.9302358

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kamar, D., Akyol, G., Mertan, A., & Inceoglu, A. (2020). Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings Makale 9302358 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU49456.2020.9302358

Kamar, Dogay ; Akyol, Gamze ; Mertan, Alican et al. / Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi. 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

@inproceedings{438d86e28a4d49738883013ed4367601,

title = "Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi",

abstract = "In this study, reinforcement learning algorithms are compared in TORCS simulation environment. In this simulation environment, the goal is to finish the track as soon as possible by controlling the car. The agent decides actions by using highlevel observations from the environment. For this goal, two reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results are compared and analyzed. Since the action space is continuous, DDPG algorithm performed better as expected. However, we were able to show that DQN algorithm also gives comparable results.",

keywords = "ddpg, double dqn., dqn, reinforcement learning",

author = "Dogay Kamar and Gamze Akyol and Alican Mertan and Arda Inceoglu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 28th Signal Processing and Communications Applications Conference, SIU 2020 ; Conference date: 05-10-2020 Through 07-10-2020",

year = "2020",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings",

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Kamar, D, Akyol, G, Mertan, A & Inceoglu, A 2020, Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi. in 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings., 9302358, 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Turkey, 5/10/20. https://doi.org/10.1109/SIU49456.2020.9302358

Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi. / Kamar, Dogay; Akyol, Gamze; Mertan, Alican et al.
2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9302358 (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi

AU - Kamar, Dogay

AU - Akyol, Gamze

AU - Mertan, Alican

AU - Inceoglu, Arda

PY - 2020/10/5

Y1 - 2020/10/5

N2 - In this study, reinforcement learning algorithms are compared in TORCS simulation environment. In this simulation environment, the goal is to finish the track as soon as possible by controlling the car. The agent decides actions by using highlevel observations from the environment. For this goal, two reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results are compared and analyzed. Since the action space is continuous, DDPG algorithm performed better as expected. However, we were able to show that DQN algorithm also gives comparable results.

AB - In this study, reinforcement learning algorithms are compared in TORCS simulation environment. In this simulation environment, the goal is to finish the track as soon as possible by controlling the car. The agent decides actions by using highlevel observations from the environment. For this goal, two reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results are compared and analyzed. Since the action space is continuous, DDPG algorithm performed better as expected. However, we were able to show that DQN algorithm also gives comparable results.

KW - ddpg

KW - double dqn.

KW - dqn

KW - reinforcement learning

UR - https://www.scopus.com/pages/publications/85100298382

U2 - 10.1109/SIU49456.2020.9302358

DO - 10.1109/SIU49456.2020.9302358

M3 - Konferans katkısı

AN - SCOPUS:85100298382

T3 - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

BT - 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th Signal Processing and Communications Applications Conference, SIU 2020

Y2 - 5 October 2020 through 7 October 2020

ER -

Kamar D, Akyol G, Mertan A, Inceoglu A. Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi. In 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9302358. (2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings). doi: 10.1109/SIU49456.2020.9302358

Pekistirmeli Ogrenme Algoritmalarinin TORCS Ortaminda Karsilastirmali Analizi

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap