Research on Dispersion Strategy for Multiple Unmanned Ground Vehicles Based on Auction Multi-agent Deep Deterministic Policy Gradient
Multiple Unmanned Ground Vehicle(multi-UGV)dispersion is commonly used in military combat missions.The existing conventional methods of dispersion are complex,long time-consuming,and have limited applicability.To address these problems,a multi-UGV dispersion strategy is proposed based on the AUction Multi-Agent Deep Deterministic Policy Gradient(AU-MADDPG)algorithm.Founded on the single unmanned vehicle model,the multi-UGV dispersion model is established based on deep reinforcement learning.Then,the MADDPG structure is optimized,and the auction algorithm is used to calculate the dispersion points corresponding to each unmanned vehicle when the absolute path is shortest to reduce the randomness of dispersion points allocation.Plan the path according to the MADDPG algorithm to improve training efficiency and running efficiency.The reward function is optimized by taking into account both during and the end of training process to consider the constraints comprehensively.The multi-constraint problem is converted into the reward function design problem to realize maximization of the reward f unction.The simulation results show that,compared with the traditional MADDPG algorithms,the proposed algorithm has a 3.96%reduction in training time-consuming and a 14.5%reduction in total path length,which is more effective in solving the decentralized problems,and can be used as a general solution for dispersion problems.
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维普
