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

Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation