Vehicle dynamic dispatching using curriculum-driven reinforcement learning

Zhang, Xiaotong; Xiong, Gang; Ai, Yunfeng; Liu, Kunhua; Chen, Long

doi:10.1016/j.ymssp.2023.110698

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…Combining the performance of different algorithms across various intersections, it can be observed that the offline learning methods MFQ-ATSC and DQN exhibit faster convergence compared to the online learning methods MFAC-ATSC and A3C [19]. Transforming the multi-intersection traffic signal control problem within the region into interactions between individual intersections and neighboring intersections enhances the convergence speed of offline learning algorithms [20]. During the experience replay process, it allows for faster learning of the optimal policy from non-current strategy experiences [21] and leads to a more stable convergence of the optimal policy [22].…”

Section: Simulation Experiments Results Analysismentioning

confidence: 99%

Mean Field Multi-Agent Reinforcement Learning Method for Area Traffic Signal Control

Zhang,

Liu

et al. 2023

Electronics

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Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches.

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