基于部分可观蒙特卡洛树搜索算法的无人系统异步任务规划OA

Unmanned systems are profoundly reshaping social lifestyles and modes of warfare.In the field of dy-namic planning for unmanned systems,the environment is first abstracted as a topological network composed of nodes and edges.Second,for the variable step time advancement problem of asynchronous planning,a novel asyn-chronous planning algorithm,namely,a partially observable Monte Carlo tree search algorithm in the semi-Markov environment(SPOMCP)is proposed.The innovation is that the topological network is transformed into a sub-goal graph with the simplest information representation,and enabling rapid policy optimization based on a variable step time advancement mechanism.Through theoretical analysis,it is proven that SPOMCP can generate the optimal strategies,and the computational complexity is exponentially correlated with the number of sub-goal nodes.Finally,simulation experiments demonstrate that SPOMCP outperforms the benchmark algorithm in terms of performance,with less than 89.18%of the benchmark algorithm＇s computation time,resulting in higher average rewards.