一种基于多Agent强化学习的多星协同任务规划算法

摘要

在分析任务特点和卫星约束的基础上给出了多星协同任务规划问题的数学模型.引入约束惩罚算子和多星联合惩罚算子对卫星Agent原始的效用值增益函数进行改进,在此基础上提出了一种多卫星Agent强化学习算法以求解多星协同任务分配策略,设计了基于黑板结构的多星交互方式以降低学习交互过程中的通信代价.通过仿真实验及分析证明该方法能够有效解决多星协同任务规划问题.%A multi-satellite cooperative planning problem model was given considering the characteristics of the task requests and satellite constraints. Then the original performance function of each satellite agent was modified by introducing both the constraint punishing operator and the multi-satellite joint punishing operator. Next, a multi-satellite reinforcement learning algorithm (MUSARLA)was proposed to derive the coordinated task allocation strategy. Furethermore, the interaction among multiple satellites was designed based on blackboard architecture to reduce the communication cost while learning. Fimally, simulated experiments are carried out which verified the effectiveness of the proposed algorithm.