# Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning
## 5 Discussion and Conclusions  
Our study introduces a reinforcement learning framework and employs a multi-agent DQN. Two agents are used, each based on different income levels. With the peer effect of EV adoption and the influence of climate awareness regarding purchasing EVs in mind, our reinforcement learning environment measures regional EV adoption scenarios. We assessed the extent to which tiered incentives should be given to low-income communities to catch up with their peers regarding EV adoption. Our findings can be summarized as follows: First, EV adoption is increasing rapidly in Austin, TX, with the adoption rate of BEVs approximately twice as high as that of PHEVs. The city's EV adoption rate is projected to surpass the forecasts made by government agencies sooner than expected. However, EVs are primarily registered in predominantly high-income and White neighborhoods. Currently, communities of color have a relatively lower rate of EV adoption. While Austin currently primarily operates a universal ITC program, there is a need to provide tiered incentives similar to the CVA program. One notes that rebate programs should target not only the first EV adopters but also consider second-hand EV owners [19]. A new program launched by the City of Riverside in California might be replicable. Second, reinforcement learning provides interpretable simulation results. Our model suggested that low-income communities are likely to stay within the EV adoption rates of high-income groups. Even with the opportunity for low-income communities to mimic the actions of high-income communities, a structural gap exists in the default setting. Indeed, offering tiered incentives increases their likelihood of bridging this gap. If providing a $1000 subsidy equates to a 12-15% impact on EV purchases [17], offering an amount of $1500-2000 might be a feasible way to ensure their adoption rates. This amount aligns with the actual amount given in the City of Riverside. However, tiered incentives should be carefully addressed to prevent moral hazards. It is worth noting that setting the boundaries of recipients and interest rates [20] are significant factors to consider. We should consider other policy initiatives that can be implemented and have effects equivalent to the identified subsidy amount. Nevertheless, we would like to acknowledge the simplified environment for the two agents. EV adoption is influenced by multiple factors and exhibits complexity, such as electricity price [41], brand utility [42; 43], consumer awareness [44], range anxiety [45], public EVCS [11; 47; 48], and the built environment [49; 50]. Reinforcement learning is designed to solve complex real-world problems, but perfectly reflecting every detail is practically impossible. Therefore, simplification to a certain level was necessary depending \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Agent** & **Best Episode** & **Promoted PHEV** & **Promoted BEV** \\ \hline \multirow{2}{*}{Default} & H & 56 & 800 & 4800 \\ \cline{2-5}  & L & 69 & 1000 & 3700 \\ \hline \multirow{2}{*}{Subsidy Effect of 10\%} & H & 66 & 800 & 4800 \\ \cline{2-5}  & L & 32 & 1000 & 4200 \\ \hline \multirow{2}{*}{Subsidy Effect of 20\%} & H & 67 & 800 & 4800 \\ \cline{2-5}  & L & 93 & 1200 & 3700 \\ \hline \multirow{2}{*}{Subsidy Effect of 30\%} & H & 63 & 800 & 4800 \\ \cline{2-5}  & L & 96 & 1300 & 3700 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mode choice in best episode. on the aim and scope of the research, which enhances the model's feasibility for learning. Factors associated with EV adoption are mainly associated with non-linearity [14; 44; 48], and addressing these factors requires the specification of distinct layers. As we have demonstrated by testing reinforcement learning to address EV adoption disparity issues, future studies could extend by adding specifications focusing on particular aspects. Then, we will be able to improve practices gradually.