Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative environment. In this work, we present techniques for centralized training of Multi-Agent Deep Reinforcement Learning (MARL) using the model-free Deep Q-Network (DQN) as the baseline model and communication between agents. We present two novel, scalable and centralized MARL training techniques (MA-MeSN, MA- BoN), which achieve faster convergence and higher cumulative reward in complex domains like autonomous driving simulators. Subsequently, we present a memory module to achieve a decentralized cooperative pol- icy for execution and thus addressing the challenges of noise and com- munication bottlenecks in real-time communication channels. This work theoretically and empirically compares our centralized and decentralized training algorithms to current research in the field of MARL. We also present and release a new OpenAI-Gym environment which can be used for multi-agent research as it simulates multiple autonomous cars driving on a highway. We compare the performance of our centralized algorithms to existing state-of-the-art algorithms, DIAL and IMS based on cumu- lative reward achieved per episode. MA-MeSN and MA-BoN achieve a cumulative reward of at least 263% of the reward achieved by the DIAL and IMS. We also present an ablation study of the scalability of MA- BoN showing that it has a linear time and space complexity compared to quadratic for DIAL in the number of agents.