Improving the performance of flocking control policies in practical scenarios is of great value in promoting the practical application of multiagent flocking collaborative control algorithms. In this article, concerning the practicality of flocking algorithms in stochastic communication environments, we propose a model learning based multiagent flocking control algorithm. First, an agent motion model construction method based on sequential attention mechanisms is proposed to provide a more realistic agent motion model for environmental interaction. Considering the cooperation and equivalence of agents in the flocking task, a multiagent cooperative soft actor–critic (MACSAC) algorithm is proposed to optimize the control policy model. Then, a digital learning system for multiagent flocking collaborative control is constructed by combining the learned motion model with the MACSAC algorithm. Finally, we design a behavior reasoning (BR) model based on the prior control policy, and introduce the model into the MACSAC algorithm to infer the motion state of noncommunicating adjacent agents, which solves the problem of poor control policy caused by the information loss of observation state in stochastic communication environments. The experimental results indicate that the constructed digital learning system can effectively simulate the policy learning of multiagent flocking in actual environmental scenarios, and demonstrate that the designed BR model can effectively improve the performance of the MACSAC-based multiagent flocking collaborative control algorithm in stochastic communication environments.