  One effective way to optimize the offloading process is by minimizing the
transmission time. This is particularly true in a Vehicular Adhoc Network
(VANET) where vehicles frequently download and upload High-definition (HD) map
data which requires constant updates. This implies that latency and throughput
requirements must be guaranteed by the wireless system. To achieve this,
adjustable contention windows (CW) allocation strategies in the standard
IEEE802.11p have been explored by numerous researchers. Nevertheless, their
implementations demand alterations to the existing standard which is not always
desirable. To address this issue, we proposed a Q-Learning algorithm that
operates at the application layer. Moreover, it could be deployed in any
wireless network thereby mitigating the compatibility issues. The solution has
demonstrated a better network performance with relatively fewer optimization
requirements as compared to the Deep Q Network (DQN) and Actor-Critic
algorithms. The same is observed while evaluating the model in a multi-agent
setup showing higher performance compared to the single-agent setup.
