A novel optimization scheduling method for integrated energy systems (IES) based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm was proposed to address the cooperative scheduling of multi-type energy storage. With the accelerating global energy transition and large-scale integration of renewable energy, the coordinated dispatch of multi-type energy storage in IES has become a key issue for improving both system economy and renewable energy utilization. The system was configured with a three-segment “source-conversion-load” structure: on the source side, the grid, photovoltaic, and wind power were integrated; on the conversion side, multi-source grid integration was enabled through power conversion devices; on the load side, three types of energy storage devices—electrochemical energy storage (EES), compressed air energy storage (CAES), and supercapacitors (SC) were deployed, forming a multi-energy complementary system in combination with user loads. To overcome the limitations of traditional optimization methods in handling high-dimensional nonlinear constraints, a multi-agent cooperative game model was constructed, through which coordinated control of energy storage devices was achieved by a centralized training and decentralized execution mechanism. Each agent was enabled to determine its charging and discharging strategy independently via an Actor-Critic network, while global state information was shared within the Critic network to optimize the joint action reward, including electricity transaction costs, penalties for wind and solar curtailment, and other relevant terms. Simulation results show that the MADDPG-based method significantly enhances system economic performance and renewable energy consumption under dynamic electricity prices and fluctuating renewable generation, thereby providing a promising approach for the efficient scheduling of multi-type energy storage systems.