UAV Path Planning Based on Multi-agent Deep Reinforcement Learning

To solve the path planning problem of multi-unmanned aerial vehicle (UAV) in complex environment, a multi-agent deep reinforcement learning UAV path planning framework was proposed. First, the path planning problem was modeled as a partially observable Markov decision process, and then, it was extended to multi-agent by using the proximal strategy optimization algorithm. Specifically, the multi-UAV barrier-free path planning was achieved by designing the UAV's state observation space, action space and reward function. Moreover, to adapt to the limited computing resource conditions of UAVs, a network pruning-based multi-agent proximal policy optimization (NP-MAPPO) algorithm was proposed, which improved the training efficiency. Simulations verify the effectiveness of the proposed multi-UAV path planning framework under various parameter configurations and the superiority of NP-MAPPO algorithm in training time.