With the development of intelligent warfare, the fragmentation and uncertainty of real-time information in highly competitive scenarios such as military operations and anti-terrorism assault put forward higher requirements for making flexible policy with game advantages. The research of intelligent policy learning method with self-learning ability has become the core issue of formation-level tasks. Faced with difficulties in state representation and low data utilization efficiency, a sample adaptive policy learning method is proposed based on predictive coding. The auto-encoder model is applied to compress the original task state space, and the predictive coding of the dynamic environment is obtained through the state transition samples of the environment combined with the autoregressive model using the mixed density distribution network, which improves the capacity of the task state representation. Temporal difference error is utilized by the predictive-coding-based sample adaptive method to predict the value function, which improves the data efficiency and accelerates the convergence of the algorithm. To verify its effectiveness, a typical air combat scenario is constructed based on the previous national wargame competition platforms, where five specially designed rule-based agents are included by the contestants. The ablation experiments are implemented to verify the influence of different factors with regard to coding strategies and sampling policies while the Elo scoring mechanism is adopted to rank the agents. Experimental results confirm that MDN-AF, the sample adaptive algorithm based on predictive coding,reaches the highest score with an average winning rate of 71%, 67.6% of which are easy wins. Moreover, it has learned four kinds of interpretable long-term strategies including autonomous wave division, supplementary reconnaissance, “snake” strike and bomber-in-the-rear formation. In addition, the agent applying this algorithm framework has won the national first prize of 2020 National Wargame Competition.