Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
This work addresses the challenge of training autonomous vehicles efficiently, but it is incremental as it applies an existing RL method to a specific simulation scenario.
The authors tackled the problem of autonomous driving by using Deep Q-Learning to develop a policy for lane-keeping and collision avoidance at high speeds, and they tested this policy in the CARLA simulation environment.
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.