Highway bottlenecks caused by temporary lane closures can lead to severe congestion and capacity drop, especially under high traffic demand. This paper proposes a learning-based Lagrangian traffic control framework in which a small fraction of connected and automated vehicles (CAVs) in mixed traffic are organized into specially structured multi-lane fleets and utilized as mobile actuators to mitigate congestion. A hierarchical control architecture is developed, where an upper-level reinforcement learning (RL) agent determines fleet-level control parameters, including cruising speed and inter-vehicle spacing, while a lower-level model predictive control (MPC) module ensures real-time trajectory tracking and safety. The proposed method explicitly captures cross-lane CAV cooperation within fleets through the design of the RL action space, and incorporates CAV-induced traffic regulation effects into the reward formulation to optimize traffic dynamics and alleviate bottleneck congestion. Simulation results in a multi-lane highway scenario demonstrate that the proposed strategy significantly improves traffic performance across a wide range of demand conditions, reducing travel delay and congestion length while increasing average traffic speed. In addition, the method exhibits generalization across varying traffic demands and improved performance with increased availability of CAV fleets.