Variable speed limits (VSL) have been widely implemented to alleviate highway congestion and enhance operational efficiency. However, most existing studies focus on fixed traffic scenarios, making them inadequate when addressing uncertainties such as fluctuating traffic flows, extreme weather conditions, and construction-induced closures. Consequently, traditional VSL control strategies exhibit limited adaptability and generalization capability in unfamiliar scenarios. To overcome these limitations, this paper proposes a VSL control strategy based on Meta-Reinforcement Learning (Meta-RL) and Multi-Agent Proximal Policy Optimization (MAPPO) (Meta-MAPPO). This method leverages the meta-learning mechanism of Meta-RL and integrates a Hypernetwork module to dynamically adjust the network parameters of the control policy. By doing so, it adapts to diverse traffic scenarios and environmental disturbances, facilitating rapid policy transfer across scenarios and enhancing control performance. The training results demonstrate that Meta-MAPPO achieves faster convergence and superior model performance than MAPPO and Meta Multi-Agent Soft Actor-Critic (Meta-MASAC). Simulation experiments reveal that, compared with traditional feedback control methods and conventional multi-agent RL approaches, Meta-MAPPO exhibits significant advantages in unseen scenarios: it effectively mitigates traffic congestion and substantially reduces total travel time. The findings provide a more applicable solution for the practical implementation of VSL and offer valuable insights for further exploration of multi-agent methodologies in intelligent transportation systems.
Zhao et al. (Thu,) studied this question.