Aiming at the problem of low recognition rate of single-agent in deep Q-learning for fault diagnosis of planetary gearbox, using multi-agent for strategy learning and fuzzy integral for fusing decision results of multiple-agent, a planetary gearbox fault diagnosis method based on multi-agent deep Q-learning and fuzzy integral was proposed.Firstly, the continuous wavelet transform (CWT) and S transform (ST) were performed for vibration signals to obtain the corresponding time-frequency feature matrix, and then using the original time domain data and the obtained time-frequency feature matrix, a multi-domain environmental state space was constructed to interact with multi-agent.Secondly, the original time-domain features, CWT time-frequency features and ST features returned by the environment space were taken as the corresponding agent state in interaction process.The Q function value of each agent was maximized with the deep Q-learning algorithm to obtain the optimal strategy.Finally, fuzzy integral was used to fuse the decision results of multi-agent, and obtain the final diagnosis results.The fault data of planetary gearbox were used to do verification and analysis.It was shown that the proposed planetary gearbox fault diagnosis method based on multi-agent deep Q-learning and fuzzy integral can effectively improve the diagnosis accuracy; its diagnosis results are better than those of the method based on single-agent deep Q-learning.