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Vision-based behavior for UAV reactive avoidance by using a reinforcement learning method

Reactive behavior is a classic control pattern in both robotic and biological systems. Inspired by this conception, this paper focuses on the mapping from the visual sensor to the actor controller by using a reinforcement learning method. This paper proposes an actor-critic algorithm based on the RBF neural network to achieve the unmanned aerial vehicle (UAV) avoidance ability. A semi-physical experiment is implemented to verify the effectiveness of the proposed algorithm.