## 4 Experiment
### Experimental Results  
As shown in Figure 11, based on the topology resulting from the partitioning of optical interconnection parallel subnetworks, a simulation platform \"Laser Communication Satellite Network Simulation Platform (LCSN-EP) [35]\" for laser satellite interconnection was utilized, relying on the SGP4 model. This platform determined the end-to-end delay of satellite data transmission between parallel subnetworks that are visible and communicable to each other, as the optical link distance varied. The data indicate that when the distance between satellites is large, data performance is excellent. This is because longer distances provide greater advantages for long-distance links by reducing the number of hops and propagation distances. This paper solved the forwarding scheduling of data packets from two perspectives: optimizing the total task burden of the satellite constellation and the maximum completion time of packet forwarding within the satellite constellation. Figure 12a,b demonstrate the execution process of reinforcement learning in solving the transmission scheduling of satellite subnets. Initially, during the iterations, the algorithm's initialization strategy randomly selected scheduling rules, leading to excessively large values for the total task burden and maximum completion time. Figure 11: Inter-subnet end-to-end latency for optical interconnection. Figure 10: Model computation time as a function of the number of satellites. As the number of iterations increased, the algorithm gradually improved its strategy, resulting in the optimal solution for the maximum completion time decreasing. The overall numerical trend exhibits a negative slope, indicating that the strategy optimized by the algorithm is effectively solving the FJSP. This discussion not only validates the effectiveness of the proposed framework for constructing inter-subnet scheduling strategies based on reinforcement learning but also confirms the convergence of reinforcement learning in solving the FJSP. To evaluate the performance of the PSFRS algorithm used in this paper for solving large-scale network problems, experiments were conducted. The algorithm proposed in this paper was compared with the Satisfiability Modulo Theories (SMT) solving method and Particle Swarm Optimization (PSO). To ensure the reliability of the experiments, each solving method was used to schedule 0-32 data packets, with the completion time as the performance metric. As shown in Figure 13, when solving the scheduling for fewer than five data packets, all three algorithms showed relatively low solving times. However, as the number of data packets to be scheduled increased, the performance of the SMT-solving method deteriorated significantly. This degradation can be attributed to the exponential increase in the exploration space of the SMT method as the number of packets to be solved increases. In comparison to the PSO algorithm, PSFRS used in this paper demonstrated superior solving time as the number of packets to be solved increased. This advantage can be attributed to the incremental updates and greedy strategy employed by Q-learning, where the algorithm selects the action with the maximum Q-value at each step, thus improving efficiency and shortening computation time. The success rate of transmission represents the proportion of successfully transmitted packets to the total number of packets sent during transmission. It is a crucial metric for evaluating the performance of routing algorithms and serves as an indicator of routing algorithm reliability. This paper focuses on time-sensitive operations, where the transmission time of packets is limited. To assess the transmission success rate of the routing scheduling algorithm employed in this paper, experiments were conducted. As shown in the simulation results in Figure 14, the transmission success rate of all algorithms decreases as the number of data packets increases.