This work mainly studies the problem of using multiple unmanned aerial vehicles (UAVs) to search for victims cooperatively in indoor scenes where the location information of victims relying on the global positioning system may be unreliable. To this end, this study proposes a multi-agent reinforcement learning (MARL) based solution which focuses on the path planning studies when the UAV team assists the rescue. Compared with the traditional solution, the proposed solution has advantages in large-scale indoor rescue scenes, such as deploying multiple rescue UAVs and rescuing multiple victims. At the same time, this solution also considers the charging problem of the UAVs to ensure that the power of the UAVs is always sufficient. Specifically, due to the continuous changes of the rescue scene depth parameters in the model, the proposed solution simulates the path planning as a decentralized partially observable Markov decision process (Dec-POMDP). To optimize the UAV control strategy, this study also trains a double deep Q-learning network (Double DQN). Finally, the Monte Carlo method is used to verify that this solution can effectively cooperate with multiple UAVs in a large-scale indoor environment and maximize the collection of the location information stored in the mobile phone used by the victim.