Firepower-target assignment method based on deep reinforcement learning algorithm

Aiming at the characteristics of large solution space, discrete, dynamic and nonlinear of firepower-target assignment problem, this paper proposes a deep reinforcement learning algorithm based on DQN. By combining the 6-layer fully connected feedforward neural network with the Q-learning algorithm, the perception ability of deep learning and the decision-making ability of reinforcement learning are fully utilized. Through the comparison of model performance tests, this method has strong fitting ability, fast convergence speed and small variance jitter, and the distribution results meet the combat expectations, which can provide some reference for commanders to make decisions on fire strike problems.