Deep Reinforcement Learning has led us to newer possibilities in solving complex control and navigation related tasks. The paper presents Deep Reinforcement Learning autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Q Network to a simulated car an urban environment. “The car, using a variety of sensors will be easily able to detect pedestrians, objects will allow the car to slow or stop suddenly. As a computer is far more precise and subject to fewer errors than a human, accident rates may reduce when these vehicles become available to consumers. This autonomous technology would lead to fewer traffic jams and safe road”.
Deep Reinforcement Learning (DRL), Reinforcement Learning (RL)
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