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

• URL: http://arxiv.org/abs/2007.10960v1
• Date: Tue, 21 Jul 2020 17:26:20 GMT
• Title: Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
• Authors: Siavash Alemzadeh, Ramin Moslemi, Ratnesh Sharma, and Mehran Mesbahi
• Abstract summary: We study adaptive data-guided traffic planning and control using Reinforcement Learning (RL) We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making.
• Score: 1.3999481573773072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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