## 6 Experiment and Analysis
### Advantages and Disadvantages of RL in CSLP  
In this section, we aim to provide a balanced discussion on the advantages and disadvantages of deploying RL in the CSLP. Understanding the strengths and limitations of RL in this context is crucial to its effective application and continual development. The advantages of RL in the CSLP are primarily characterized by its adaptability, scalability, and ability to balance between exploration and exploitation. RL's adaptability \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Gain Score** & **Benefit Score** & **Waiting Time** & **Travel Time** & **Charging Time** \\ \hline Existing Charging & 100 & 100 & 100 & 100 & 100 \\ GA & 163.35 & 121.28 & 72.11 & 86.89 & 95.21 \\ Greedy Benefit first & 179.68 & 125.97 & 62.25 & 88.88 & 89.10 \\ Greedy, Demand\_first & 145.09 & 115.58 & 82.71 & 96.56 & 92.25 \\ PPO-Attention (Ours) & **258.04** & **148.18** & **26.60** & **81.53** & **59.90** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average results for all datasets. The best scores are highlighted in bold. Figure 4: The planning results for five cities from the PPO-Attention algorithm. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Score \(\uparrow\)**} & \multicolumn{3}{c}{**Cost \(\downarrow\)**} \\ \cline{2-7}
**Algorithm** & **Gain** & **Benefit** & **Wait** & **Travel** & **Charging** & **Travel** & **Wait Max [min]** & **Wait Max** \\ \hline \multicolumn{7}{c}{Rouen, France} \\ \hline Existing Changing & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 7.74 & - \\ GA & 141.00 & 108.70 & 99.00 & 80.88 & 101.58 & **5.34** & - \\ Greedy Benefit first & 135.20 & 107.39 & 65.12 & 96.83 & 91.81 & 7.80 & - \\ Greedy-Demand\_first & 113.19 & 104.85 & 100.00 & 100.27 & 93.29 & 7.74 & - \\ PPO-Attention (Ours) & **248.67** & **139.55** & **8.29** & **78.03** & **65.48** & 6.81 & **147.60** \\ \hline \multicolumn{7}{c}{Culver City, California, United States} \\ \hline Existing Charging & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 & 2.91 & 217.19 \\ GA & 117.17 & 108.41 & 100.36 & 100.00 & 101.62 & 2.91 & 217.19 \\ Greedy Benefit first & 134.20 & 105.09 & 53.28 & 96.52 & 94.67 & 2.88 & 93.60 \\ Greedy-Demand\_first & 112.55 & 102.57 & **88.94** & 96.49 & 95.63 & 2.91 & 205.64 \\ PPO-Attention (Ours) & **221.04** & **130.53** & **19.13** & **83.91** & **61.82** & **2.18** & **28.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on the Rouen, France, and Culver City, California, United States datasets. Evaluation metrics favoring higher values are indicated with \(\uparrow\), while those favoring lower values are labeled with \(\downarrow\). The best scores are highlighted in bold. allows for continual learning and adaptation to changing environments, such as variable EV adoption rates or alterations in road network complexity. This adaptability enables the model to optimize charging station placement as conditions change. Furthermore, RL algorithms demonstrate excellent scalability, being well-suited to managing large and complex road networks. As the size and complexity of the problem scale, RL algorithms can still find optimal solutions, making them a powerful tool for CSLP. Finally, RL's ability to balance between exploration (searching new possible charging station locations) and exploitation (leveraging knowledge of locations that are already known to be effective) results in a robust solution that can discover and capitalize on optimal charging station locations. However, RL application in the CSLP also presents challenges related to data requirements, interpretability, and computational expense. RL algorithms often require substantial data for training. In the context of CSLP, obtaining sufficient and accurate data on traffic patterns, EV adoption rates, and driver behavior can be challenging. Additionally, the decision-making process of RL algorithms can be opaque, presenting difficulties in interpreting why certain locations were chosen for charging stations over others.