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Traffic signal control using a cooperative EWMA-based multi-agent reinforcement learning

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Abstract

In contemporary urban, traffic signal control is still enormously difficult. Multi-agent reinforcement learning (MARL) is a promising ways to solve this problem. However, most MARL algorithms can not effectively transfer learning strategies when the agents increase or decrease. This paper proposes a new MARL algorithm called cooperative dynamic delay updating twin delayed deep deterministic policy gradient based on the exponentially weighted moving average (CoTD3-EWMA) to solve the problem. By introducing mean-field theory, the algorithm implicitly models the interaction between agents and environment. It reduces the dimension of action space and improves the scalability of the algorithm. In addition, we propose a dynamic delay updating method based on the exponentially weighted moving average (EWMA), which improves the Q value overestimation problem of the traditional TD3 algorithm. Moreover, a joint reward allocation mechanism and state sharing mechanism are proposed to improve the global strategy learning ability and robustness of the agent. The simulation results show that the performance of the new algorithm is better than the current state-of-the-art algorithms, which effectively reduces the delay time of vehicles and improves the traffic efficiency of the traffic network.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61973244, 72001214).

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  1. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Zhimin Qiao, Liangjun Ke & Xiaoqiang Wang

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  1. Zhimin Qiao
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Correspondence to Liangjun Ke.

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Qiao, Z., Ke, L. & Wang, X. Traffic signal control using a cooperative EWMA-based multi-agent reinforcement learning. Appl Intell 53, 4483–4498 (2023). https://doi.org/10.1007/s10489-022-03643-9

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