.png){kind=icon}
返回
Imagination-Augmented Reinforcement Learning Framework for Variable Speed Limit Control
.png){kind=icon}
OA
AI
DOI：10.1109/TITS.2023.3316285.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Variable Speed Limit (VSL) is a commonly applied active traffic management measure for urban motorways. In recent years, model-based and model-free approaches have been extensively adopted to solve VSL optimization problems. However, the success of model-based VSL relies heavily on the nature of the environmental model adopted (e.g., traffic flow model). Implicit environment models may result in inappropriate control actions. Although model-free approaches are able to directly map raw measurements to control actions without a need for an environment model, they usually require large amounts of training data. In order to address these issues, we propose an Imagination-Augmented Agent (I2A) for VSL control. The I2A consists an imagination path and a model-free path, which work together to generate appropriate control actions. The simulation results show that the proposed I2A agent outperforms other tested Reinforcement Learning (RL) agents in terms of Total Time Spent and bottleneck volume.
Keyword:
Q-learning
Mathematical models
Optimization
Heuristic algorithms
Computational modeling
Aerospace electronics
Detectors
Motorway control
variable speed limit
deep reinforcement learning
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
.png){kind=icon}
分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Reactions of Dianionic α-Diimine-Supported Dimagnesium(I) Compound [K(THF)3]2[LMg–MgL] with Nitriles
PREAI
分享收藏
查看更多内容