In recent years, multi-agent reinforcement learning methods have demonstrated excellent decision-making capabilities and broad application prospects in successful cases such as AlphaStar, AlphaDogFight, and AlphaMosaic. In the multi-agent decision-making system in a real-world environments, the decision-making space of its task is often a parameterized action space with both discrete and continuous action variables. The complex structure of this type of action space makes traditional multi-agent reinforcement learning algorithms no longer applicable. Therefore, researching for parameterized action spaces holds important significance in real-world application. This study proposes a factored multi-agent centralised policy gradients algorithm for parameterized action space in multi-agent settings. By utilizing the factored centralised policy gradient algorithm, effective coordination among multi-agent is ensured. After that, the output of the dual-headed policy in the parameterized deep deterministic policy gradient algorithm is employed to achieve effective coupling in the parameterized action space. Experimental results under different parameter settings in the hybrid predator-prey scenario show that the algorithm has good performance on classic multi-agent parameterized action space collaboration tasks. Additionally, the algorithm’s effectiveness and feasibility is validated in a multi-cruise-missile collaborative penetration tasks with complex and high dynamic properties.