关键词: 多目标火力规划, 近端策略优化算法, 长短期记忆网络, 序贯决策

Abstract:

To solve the problem of multi-target firepower planning in defensive combat scenarios under high dynamic battlefield situation, a firepower planning method based on the proximal strategy optimization algorithm is proposed. With the goal of maximizing combat effectiveness, the reinforcement learning reward function is designed from four aspects: ammunition consumption, combat effect, combat cost and combat time. Considering the influence of historical decision sequence on the current planning, the neural network is designed based on the Actor-Critic framework with the long short-term memory network (LSTM) as the core. The network is trained by the proximal strategy optimization algorithm, and the trained reinforcement learning agent is used for sequential decision-making. A series of coherent fire planning schemes are generated in real time according to the situation of multiple decision-making stages. Simulation results show that the agent can realize multi-target firepower planning under high dynamic situation, and its computational efficiency has more obvious advantages than other algorithms.

Key words: multi-target firepower planning, proximal strategy optimization algorithm, long short-term memory network (LSTM), sequential decision-making