Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond

Siavash Alemzadeh; Ramin Moslemi; Ratnesh Sharma; Mehran Mesbahi

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arXiv · 2007.10960

Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond

Siavash Alemzadeh·Ramin Moslemi·Ratnesh Sharma·Mehran Mesbahi

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Abstract

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in our algorithm that improve the original Deep Q-Networks (DQN) for discrete control and discuss the traffic-related interpretations that follow. We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making. We introduce a new form of reward function which is further discussed using illustrative examples with comparisons to traditional traffic control methods.

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Siavash Alemzadeh, Ramin Moslemi, Ratnesh Sharma, Mehran Mesbahi. 2020-07-21. Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond. https://arxiv.org/abs/2007.10960

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