To　solve　the　path　planning　problem　of　multi-unmanned　aerial　vehicle　(UAV)　in　complex　environment,　a　multi-agent　deep　reinforcement　learning　UAV　path　planning　framework　was　proposed.　First,　the　path　planning　problem　was　modeled　as　a　partially　observable　Markov　decision　process,　and　then,　it　was　extended　to　multi-agent　by　using　the　proximal　strategy　optimization　algorithm.　Specifically,　the　multi-UAV　barrier-free　path　planning　was　achieved　by　designing　the　UAV蒺s　state　observation　space,　action　space　and　reward　function.　Moreover,　to　adapt　to　the　limited　computing　resource　conditions　of　UAVs,　a　network　pruning-based　multi-agent　proximal　policy　optimization　(NP-MAPPO)　algorithm　was　proposed,　which　improved　the　training　efficiency.　Simulations　verify　the　effectiveness　of　the　proposed　multi-UAV　path　planning　framework　under　various　parameter　configurations　and　the　superiority　of　NP-MAPPO　algorithm　in　training　time.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.