With the increasingly complex low-altitude airspace environment,the probability of conflict among UAVs performing missions is increasing.Traditional reinforcement learning algorithms of SAC and DDPG suffer from slow convergence rate and unstable convergence in solving the problem of collision avoidance among multiple UAVs in limited airspace.To solve the problems,a Multi-Agent Reinforcement Learning (MARL) method based on PPO2 algorithm is proposed.Firstly,the multi-UAV flight decision-making problem is described as a Markov decision-making process.Secondly,the state space and reward function are designed to optimize the strategy by maximizing the cumulative reward,so that the overall training is more stable and converges faster.Finally,a flight simulation scene is built based on the deep learning framework TensorFlow and the reinforcement learning environment Gym,and simulation experiments are carried out.The experimental results show that the proposed method improves the success rate of collision avoidance by about 37.74 and 49.15 percent points respectively compared with that of the SAC and DDPG algorithms,which can better solve the problem of collision avoidance among multiple UAVs,and is better in terms of convergence rate and convergence stability.