Preprint
Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
ArXiv.org
Cornell University
08/12/2022
DOI: 10.48550/arxiv.2112.13817
Abstract
Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on a simulator of urban mobility. As a difference from existing works, a policy-based deep reinforcement learning method, Proximal Policy Optimization (PPO), is utilized other than value-based methods such as Deep Q Network (DQN) and Double DQN (DDQN). At first, the obtained optimal policy from PPO is compared to those from DQN and DDQN. It is found that the policy from PPO performs better than the others. Next, instead of the fixed-interval traffic light phases, we adopt the light phases with variable time intervals, which result in a better policy to pass the traffic flow. Then, the effects of environment and action disturbances are studied to demonstrate the learning-based controller is robust. At last, we consider unbalanced traffic flows and find that an intelligent traffic light can perform moderately well for the unbalanced traffic scenarios, although it learns the optimal policy from the balanced traffic scenarios only.
Details
- Title: Subtitle
- Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
- Creators
- Yue Zhu - University of IowaMingyu CaiChris Schwarz - University of IowaJunchao Li - University of IowaShaoping Xiao - University of Iowa
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2112.13817
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 08/12/2022
- Academic Unit
- Iowa Technology Institute; Driving Safety Research Institute; Mechanical Engineering
- Record Identifier
- 9984530268902771
Metrics
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