基于强化学习的航空兵认知行为模型

航空兵的认知行为模型为仿真航空兵的空战决策提供支持,通过强化学习积累战术决策经验.在虚拟战场环境中,作战态势通过多个属性进行描述,这使得强化学习过程将面临一个高维度的问题空间.传统的空间离散化方法处理高维空间时将对计算资源和存储资源产生极大需求,因此不可用.通过构造一个基于高斯径向基函数的拟合网络解决了这个问题,大大减少了对资源的需求以及强化学习周期,并最终产生了合理的机动策略.模型的有效性和自适应性通过一对一的空战仿真进行了验证,产生的交战轨迹与人类飞行员产生的交战轨迹类似.

Cognition behavior model for air combat based on reinforcement learning

A cognition model was proposed to support tactical decisions for simulated fighters to fight with each other in a virtual combat, and reinforcement learning (RL) technology was used to acquire knowledge. The combat situation was described by multi-attributes, which resulted in a high dimensional problem space in which the fighters learned to find action policies. The traditional approach that partitioned the problem space would impose demand on huge computation and storage resource. An approximation network is constructed based on Gaussian radial basis function to approximate the state value, which greatly reduced the resource demand and learning cycle time, and produced reasonable maneuver strategy. The model was verified by a one-to-one air combat simulation, and the produced trajectories are similar with those that human pilots flied in real combat.