Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
This work addresses traffic management for urban planning, but it appears incremental as it builds on existing DQN methods with modifications.
The paper tackles adaptive traffic control by developing a novel DQN-based algorithm (TC-DQN+) that incorporates recent deep RL techniques to improve decision-making, aiming to achieve state-of-the-art performance beyond traditional methods.
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.