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Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs
2021
Complexity
A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is
doi:10.1155/2021/8818013
fatcat:xezl64j7zfeatkwik2lfy2j5le