# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 5 Experimental Results And Analysis
### Experiments And analysis  
Figure 4 presents a comparative analysis of the network computation rates across various algorithms under identical model conditions. The network computation rates of all algorithms oscillate within a defined range, signifying that each algorithm achieves a relatively stable rate over time. Specifically, TD3-based and A2C-based algorithms record lower long-term network computation rates. Although a temporary intersection exists between the LyA3C and A2C-based algorithms at a specific time, the LyA3C algorithm ultimately outperforms the rest by attaining the highest optimal network computation rate. This superiority is attributed to the LyA3C algorithm's deployment of multiple parallel agents, which enhances the network computation rate during operation. As time progresses, the fluctuation range diminishes, steadily converging toward the optimal network computation rate. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbols** & **Value** \\ \hline Antenna gain & \(A_{d}\) & 3 \\ Carrier frequency & \(f_{c}\) & 915 MHz \\ Path loss exponent & \(d_{e}\) & 3 \\ Noise power spectral density & \(v_{0}\) & \(-174\,\mathrm{dBm}/\mathrm{Hz}\) \\ Bandwidth & \(W\) & 2 MHz \\ Maximum local CPU & \(f_{n}^{\max}\) & 0.3 GHz \\ Maximum Transmit Power & \(p_{n}^{\max}\) & 0.1 watt \\ Communication overhead & \(v_{n}\) & 1.1 \\ Computation energy efficiency & \(\kappa\) & \(10^{-26}\) \\ Computation cycles per one bit & \(\phi\) & 100 \\ Memory capacity & \(q\) & 1024 \\ Energy consumption threshold & \(\xi_{n}\) & 0.04 watt \\ Lyapunov control parameter & \(V\) & 20 \\ Data arrival rate & \(\lambda_{n}\) & 2.7 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. Figure 4: Network computation rate for different algorithms. Figure 6 shows that the average data queue length tends to rise with the increase in \(\eta_{n}\). This is because a large amount of data is constantly entering the queue. Although an increase in the network computation rate can accelerate the data leaving the queue, the amount of data leaving the queue is still less than the amount of data arriving. Consequently, the average data queue length grows until it reaches maximum capacity. In Figure 7, the average energy consumption is positively correlated with the data arrival rate. Hence the average energy consumption gradually increases as \(\eta_{n}\) grows, leveling off when \(\eta_{n}\) is relatively large. Figure 8 presents the trend of network computation rates over time under various energy consumption threshold constraints \(\xi_{n}\) for all industrial terminals. It is observable that the larger the value of \(\xi_{n}\), the higher the network computation rate. The primary reason is that as \(\xi_{n}\) increases, the available energy for computation also increases, which in turn enhances the network computation rate, and ensures the long-term stability of the system. Specifically, when \(\xi_{n}=2\), the network computation rate gradually increases with time and ultimately converges to a higher value. When \(\xi_{n}=1.5\) and \(\xi_{n}=1\), although the network computation rate also increases, it eventually falls short of the value observed when \(\xi_{n}=2\). Figure 9 evaluates the changes in task queue length of the three algorithms with different energy thresholds. The figure shows that the task queue of the three algorithms decreases with the increase of the energy consumption threshold. This is because when the energy consumption threshold increases, the energy available to the industrial terminal for computation offloading increases, more tasks are processed on time, and the length of the task queue decreases. When the energy consumption threshold reaches a specific value, the industrial terminal is no longer affected by energy consumption, tasks can be freely scheduled, and the task queue length converges to a specific value.