  Nowadays, autonomous vehicles are gaining traction due to their numerous
potential applications in resolving a variety of other real-world challenges.
However, developing autonomous vehicles need huge amount of training and
testing before deploying it to real world. While the field of reinforcement
learning (RL) has evolved into a powerful learning framework to the development
of deep representation learning, and it is now capable of learning complicated
policies in high-dimensional environments like in autonomous vehicles. In this
regard, we make an effort, using Deep Q-Learning, to discover a method by which
an autonomous car may maintain its lane at top speed while avoiding other
vehicles. After that, we used CARLA simulation environment to test and verify
our newly acquired policy based on the problem formulation.
