Titlebook: Deep Learning for Unmanned Systems; Anis Koubaa,Ahmad Taher Azar Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusiv

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meritorious

Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Ga by adding an anticipator network to the original model structure. The goal of doing this is to make the agent act more like human players. It will generate anticipation before making decisions, then combine the real-time game screen with anticipation images together as a whole input of the network

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Deep Reinforcement Learning for Quadrotor Path Following and Obstacle Avoidance,ocity according to the path’s shape. For the obstacle avoidance problem, a combination of a DDPG agent that avoids obstacles and another one that follows the path is presented. The obstacle avoidance approach uses the LIDAR information to detect obstacles around the vehicle. LIDAR data is processed

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Inertia

,Detection and Recognition of Vehicle’s Headlights Types for Surveillance Using Deep Neural Networksify the vehicles which are violating the traffic laws. Various problems exist in the recognition and detection of headlights, such as erroneous detection of street lights, reflection of water in rain, sign lights and the reflection plate. Some other techniques are also used for this kind of problems

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Desk Reference for Neurosciencene learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overvi

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