## 4 Experimental Results and Analysis
### Simulation Results  
#### 4.2.1 Performance Comparison of Reward Function Convergence  
The simulation framework is shown in Figure 4. The performance comparisons of Reward function convergence for the benchmark algorithms and the proposed algorithm by varying the platoon numbers from seven to eight, and comprising five vehicles in each platoon, are plotted in Figure 5 and Figure 6, respectively. To begin with, it is evident that in  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters–Vehicular Environment** & **Values** \\ \hline Number of Resource blocks & 3 \\ Bandwidth of each Resource block & 180 kHz \\ Carrier Frequency & 2 GHz \\ Total number of vehicles & 15–50 \\ Platoon size & 5–10 \\ Intra-platoon spacing (in m) & 5, 15, 25, 35 \\ Speed of platoon & 36–54 km/h \\ Mobility model of vehicles & Urban Case [9] \\ Power of vehicles & 30 dBm \\ Noise power (\(\sigma^{2}\)) & \(-114\) dBm \\ CAM Size & 4 kB \\ CAM Transmission Time & 10 ms \\ CV and RSU capacity requirement & 3 bps/Hz \\ Fast Fading & Rayleigh fading \\ Channel information update & for every 10 ms \\ Edge node Tx power & 46 dBm \\ Edge node Antenna gain & 18 dB \\ Edge node blocks & 20 \\ \hline
**Parameters–Neural Network** & **Values** \\ \hline Mini batch size & 64 \\ Experience Replay & 50,000 \\ Discount factor \(\gamma\) & 0.99 \\ Target network update parameter & 0.0005 \\ Hidden layers of local actor network & 1024 and 512 neurons \\ Hidden layers of local critic network & 512 and 256 neurons \\ Hidden layers of local actor network & 1024, 512, and 256 neurons \\ Learning rate of critic/actor & 0.001/0.0001 \\ Episodes & 500 \\ Iterations for each episode & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
the given T seconds duration, every agent (vehicle) can effectively accomplish the assigned task and attain the appropriate rewards. Further, it can be inferred that the reward functions (task-wise) converge under 50 episodes, eventually expediting the convergence time of the proposed CMADDPG algorithm and outperforming the other benchmark algorithms. Initially, exploring the fully centralized DDPG algorithm, it delivers deprived performance when compared with all other algorithms in both cases. The algorithm has to observe all the agents and decide on the action in a centralized manner, whereas the agents' performance is not conceived explicitly, causing underperformance; the performance becomes worse as the number of agents escalates. Subsequently evaluating the next MADDPG algorithms, the decentralized behavior exacerbated the agents to be completely ignorant of the neighboring agent's action and its corresponding policies, resulting in a higher interference and deteriorating the algorithm's performance as a whole, as shown in Figure 5. Moreover, with the addition of platoons, the efficiency of the algorithm is aggravated more than that of centralized DDPG, as illustrated in Figure 6. It is apparent from the plot that the proposed CMADDPG method stabilizes quickly with few distortions while converging, primarily due to the effective cooperation of agents (vehicles) resulting in concurrent maximization of agents' global and local rewards. In addition, the proposed framework is highly resistant to the errors generated by function approximations. The robustness is attained through proper evaluation of errors while updating the policies and values by global critics and also achieved through the exploration employed with Gaussian noise. Ultimately, the aforesaid crucial factors are also liable for the convergence of the proposed CMADDPG algorithm despite the densified vehicle population, as depicted in Figures 5 and 6.