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

Translated title of the contribution: Comparative Analysis of Reinforcement Learning Algorithms on TORCS Environment

Dogay Kamar, Gamze Akyol, Alican Mertan, Arda Inceoglu

Department of Computer Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Translated title of the contribution	Comparative Analysis of Reinforcement Learning Algorithms on TORCS Environment
Original language	Turkish
Title of host publication	2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728172064
DOIs	https://doi.org/10.1109/SIU49456.2020.9302358
Publication status	Published - 5 Oct 2020
Event	28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Duration: 5 Oct 2020 → 7 Oct 2020

Publication series

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

Conference

Conference	28th Signal Processing and Communications Applications Conference, SIU 2020
Country/Territory	Turkey
City	Gaziantep
Period	5/10/20 → 7/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/SIU49456.2020.9302358

Cite this

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 Article 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).

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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.",

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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",

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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U2 - 10.1109/SIU49456.2020.9302358

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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

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