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• W3039793486 abstract "This letter presents a combination of reinforcement learning (RL) and deterministic controllers to learn a quadrotor control. Learning the quadrotor flight in a standard RL approach requires many iterations of trial and error, which may bring about risky exploration and battery consumption. In this letter, we integrate a classical controller such as PD (proportional and derivative) or LQR (linear quadratic regulator) with a RL policy using their linear combination. The proposed method is not only simple to use, but also fast in learning convergence. When the algorithm is evaluated for a quadrotor trajectory tracking by means of a velocity control for both simulation and experiment, it demonstrates the faster convergence rate and better control performance in comparison with an existing rapid model-based RL method." @default.
• W3039793486 created "2020-07-10" @default.
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• W3039793486 date "2021-04-01" @default.
• W3039793486 modified "2023-10-03" @default.
• W3039793486 title "Hybrid Reinforcement Learning Control for a Micro Quadrotor Flight" @default.
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• W3039793486 doi "https://doi.org/10.1109/lcsys.2020.3001663" @default.
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