A reinforcement learning algorithm for combat UAV path planning
In order to make it uneasy for unmanned aerial vehicles(UAVs)to be attacked by the ground am-bush of individual anti-aircraft weapons,this paper proposes a new reinforcement learning algorithm used for com-bat UAVs to perform the mission of missile avoidance,shortest path flight and formation flight.The algorithm combines self-imitation learning and stochastic network refining algorithm to enhance exploration through amplifi-cation of imitation effect(AIE).Experimental results show that the proposed algorithm is very effective in finding the shortest flight path for the combat UAV while avoiding enemy missiles,and is also superior to the existing al-gorithm in terms of convergence speed and learning stability.This provides a certain reference for the UAVs to avoid being hit by missiles.
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