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Title: A Review of Reinforcement Learning Approaches for Control of Unmanned Aerial Vehicles Subtitle: Increased Dependability and Reduced Energy Consumption for Drone Delivery Reinforcement Learning (RL) is a powerful and flexible optimization approach that enables robots to learn how to perform tasks and environments over time. Its use for unmanned aerial vehicle (UAV) autonomous flight is limited because of the high computational and energy consumption of traditional reinforcement learning approaches, such as Monte Carlo tree
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Reinforcement Learning (RL) is a machine learning problem that is concerned with learning to choose the most effective actions to perform in a given environment based on past feedback. The problem has real world applications in domains such as self-driving cars, games, finance, and healthcare, to name just a few. The course I am taking covers RL with a specific emphasis on reinforcement learning for self-driving cars. It is designed for undergraduate and graduate students interested in researching the area of reinforcement learning, as well as