Reinforcement Learning for Joint Control of Traffic Signals in a Transportation Network
- Authors
- Lee, Jincheol; Chung, Jiyong; Sohn, Keemin
- Issue Date
- Feb-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Adaptive traffic signal control; Deep Q-network; Reinforcement learning
- Citation
- IEEE Transactions on Vehicular Technology, v.69, no.2, pp 1375 - 1387
- Pages
- 13
- Journal Title
- IEEE Transactions on Vehicular Technology
- Volume
- 69
- Number
- 2
- Start Page
- 1375
- End Page
- 1387
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38551
- DOI
- 10.1109/TVT.2019.2962514
- ISSN
- 0018-9545
1939-9359
- Abstract
- Reinforcement learning (RL) approaches have recently been spotlighted for use in adaptive traffic-signal control on an area-wide level. Most researchers have employed multi-agent reinforcement learning (MARL) algorithms wherein each agent shares a holistic traffic state and cooperates with other agents to reach a common goal. However, MARL algorithms cannot guarantee a global optimal solution unless the actions of all agents are fully coordinated. The present study employs a RL algorithm that recognizes an entire traffic state and jointly controls all the traffic signals of multiple intersections. With this approach, a deep Q-network (DQN) that depends solely on traffic images is extended to overcome the curse of dimensionality that is associated with a large state and action space. Several front layers in a deep convolutional neural network (CNN) to approximate the true Q-function are shared by each intersection approach. Weight parameters connecting the last hidden layer to the output layer are fixed. The proposed methodology outperforms a fixed-signal operation, a fully actuated signal operation, a multi-agent RL control without coordination, and a multi-agent RL control with partial coordination. © 1967-2012 IEEE.
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