  Training self-driving cars is often challenging since they require a vast
amount of labeled data in multiple real-world contexts, which is
computationally and memory intensive. Researchers often resort to driving
simulators to train the agent and transfer the knowledge to a real-world
setting. Since simulators lack realistic behavior, these methods are quite
inefficient. To address this issue, we introduce a framework (perception,
planning, and control) in a real-world driving environment that transfers the
real-world environments into gaming environments by setting up a reliable
Markov Decision Process (MDP). We propose variations of existing Reinforcement
Learning (RL) algorithms in a multi-agent setting to learn and execute the
discrete control in real-world environments. Experiments show that the
multi-agent setting outperforms the single-agent setting in all the scenarios.
We also propose reliable initialization, data augmentation, and training
techniques that enable the agents to learn and generalize to navigate in a
real-world environment with minimal input video data, and with minimal
training. Additionally, to show the efficacy of our proposed algorithm, we
deploy our method in the virtual driving environment TORCS.
