| Autor: |
Timur, Muhammad Idham Ananta, Dharmawan, Andi, Istiyanto, Jazi Eko, Pambudi, Stanislaus Arya Luhur, Tsurayya, Hayfa |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2654 Issue 1, p1-8, 8p |
| Abstrakt: |
Traffic congestion is one of the most common problems in line with the increase in population and economic activity. To tackle this problem, many places in the world place a traffic light in most major or dense traffic intersections. The traffic lights, especially in Indonesia, are conventionally giving the signals; it always repeats the same cycle over time. This practice has a weakness in its inability to adapt to various traffic conditions. Reinforcement learning is a popular approach for intelligent traffic signal controller as it has advantages such as self learning without the need of supervision, goal oriented, real-time adaptation and curse of dimensionality management. One of the algorithms that can be used is Asynchronous Advantage Actor-Critic (A3C). This paper will discuss the experiments of Asynchronous Advantage Actor-Critic performance compared to stable baseline Advantage Actor-Critic performance using real-life-like traffic rules environments in SUMO with an open-source repository using the Indonesian traffic rules Environment. Results given in this study show that Asynchronous Advantage Actor-Critic (A3C) got a faster learning time and effective in gaining optimal result compared to Advantage Actor-Critic (A2C) using real-life-like traffic rules environments in SUMO with an open-source repository. Furthermore, our results indicate that the advantage of using A3C is not that visible in the single intersection scenario, but notable in the four and nine intersections scenario. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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