UAV Navigation System with Obstacle Detection using Deep Reinforcement Learning with Noise Injection

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This paper is an application of deep reinforcement learning and noise injection to the navigation of the Unmanned Aerial Vehicle. Recent studies take advantage of the enhancement of the exploratory of its navigation to the environment like civilian missions such as disaster monitoring and, search and rescue. Some research studies include noise injection in the deep learning algorithm to improve its performance. Therefore, this study applied the ad compared two ways to inject Gaussian noise namely Gaussian Noise Layer and Noisy Network in Double Dueling Deep Q Network. The tests conducted with the Gaussian Noise Layer with a standard deviation of 1.0 gives stable exploration performance in terms of q learning, loss and flight navigation. The noticeable results give the significance to further research in Gaussian Noise injection in optimizing the deep reinforcement algorithm of unmanned aerial vehicles where can be further applied to future technology of navigation.