## 1 Introduction  
Highways serve as the core of modern transportation networks, significantly impacting China's transportation system and regional economic development. However, the current construction and operation of highway infrastructure fail to effectively meet growing regional traffic demands, leading to frequent congestion and accidents. Consequently, road efficiency decreases, driving safety is compromised, and travel times are extended. Traffic bottlenecks frequently occur at the merging points of mainline and entrance ramp lanes due to lane reductions and vehicle lane changes, severely impacting vehicle speeds. Frequent deceleration, acceleration, or stoppages in these bottleneck areas result in significant delays and congestion [1]. To address the imbalance between highway traffic demand and supply, a comprehensive approach [2] that integrates technological measures, infrastructure improvements, and demand-side optimization is employed. Technological measures, such as Intelligent Transportation Systems (ITS), are used to enhance traffic operational efficiency. Infrastructure improvements, including overall network planning [3], roundabout intersections [4], and ramp optimization designs [5], are implemented to increase capacity. Demand-side optimization, through regional traffic diversion, public travel behavior guidance, and demand substitution, effectively mitigates peak traffic pressure. Among these, Intelligent Transportation Systems (ITS) are crucial for resolving highway traffic demand conflicts by providing control instructions to vehicles based on traffic flow data patterns. This approach achieves control objectives for specific road segments. For instance, Variable Speed Limit (VSL) control optimizes traffic efficiency by adjusting upstream speed limits to reduce spatiotemporal variations in traffic speed, thereby improving bottlenecks and preventing further deterioration. This method enhances traffic performance, safety, and mobility. However, vehicle weaving caused by lane changes in merging areas, aside from increasingly saturated traffic demand, also contributes significantly to bottleneck issues. Lane Change Control (LCC) calculates congestion levels in different lanes and issues mandatory lane change instructions to vehicles upstream of merging bottlenecks, optimizing lane density distribution and alleviating bottlenecks caused by vehicle weaving. VSL and LCC are complementary and synergistic methods. Implementing LCC requires that the road segment is not overly saturated; otherwise, vehicles may not respond to lane change instructions. VSL can mitigate this issue. Therefore, integrating VSL and LCC effectively addresses ramp merging bottlenecks. Numerous studies have integrated VSL and LCC to alleviate ramp merging bottlenecks. For example, [PERSON] et al. [6] found that integrating VSL and LCC stabilizes upstream vehicle density and homogenizes traffic flow, reducing frequent lane changes near bottlenecks. Additionally, LCC provides lane change recommendations for vehicles away from bottlenecks, mitigating capacity drop issues. However, previous research limited control areas to fixed and unchanging ranges, resulting in inflexible control strategies. Furthermore, when multiple entrance ramps are present, local control by different controllers impacts overall traffic due to the upstream and downstream propagation characteristics of traffic flow. Integrated control research on multiple entrance ramps is necessary to alleviate merging bottlenecks in large-scale road networks effectively. This approach mitigates congestion at each ramp and maintains the stability of overall network traffic operations. Based on the current analysis of research on highway ramp merging, this paper's main contribution lies in the combination of controllers and their integration with reinforcement learning.