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Asynchronous n-step Q-learning adaptive traffic signal control
| 题名： | Asynchronous n-step Q-learning adaptive traffic signal control |
| 正文语种： | 英文 |
| 作者： | Wade Genders; Saiedeh Razavi |
| 作者单位： | Department of Civil Engineering, McMaster University, Hamilton, Ontario, Canada |
| 关键词： | Artificial intelligence; intelligent transportation systems; neural networks; reinforcement learning; traffic signal controllers |
| 摘要： | Ensuring transportation systems are efficient is a priority for modern society. Intersection traffic signal control can be modeled as a sequential decision-making problem. To learn how to make the best decisions, we apply reinforcement learning techniques with function approximation to train an adaptive traffic signal controller. We use the asynchronous n-step Q-learning algorithm with a two hidden layer artificial neural network as our reinforcement learning agent. A dynamic, stochastic rush hour simulation is developed to test the agent’s performance. Compared against traditional loop detector actuated and linear Q-learning traffic signal control methods, our reinforcement learning model develops a superior control policy, reducing mean total delay by up 40% without compromising throughput. However, we find our proposed model slightly increases delay for left turning vehicles compared to the actuated controller, as a consequence of the reward function, highlighting the need for an appropriate reward function which truly develops the desired policy. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 23 |
| 期： | 4 |
| 页码： | 319-331 |
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