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Virtual Autonomous Vehicle Using Deep Reinforcement Learning

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Document pages: 5 pages

Abstract: In this paper, we propose an in-depth reinforcement learning approach to evaluate the performance of the virtually created autonomous vehicle driving scenario. Markov Decision Process is used to map the state of the vehicle to an action. Discount and Reward functions are also incorporated in the decision policy. To deal with high-dimensional inputs which lead to the standard instabilities of reinforcement learning, we have used experience replay. To further reduce the correlation, we use an iterative update that updates the Q-values periodically. Adam Optimizer, based on stochastic objective functions, is used as an optimizer in a neural network along with Rectified Linear Unit activation function, which helps in further optimizing the process. The autonomous vehicle doesn t need any labelled training data to learn human driving behaviour. Inspired by the real-world scenarios, an action-based reward function is used to train the vehicle. It has been shown in our approach that after some iterations, the virtually created vehicle generates collision-free movement and performs the same driving behaviour as of humans.

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