In　hole　machining,　drilling　path　optimization　(DPO)　plays　a　critical　role　in　enhancing　production　efficiency　by　minimizing　tool　path　length　through　the　strategic　sequencing　of　drilling　operations.　However,　existing　DPO　model　has　largely　neglected　the　impact　of　variability　in　pilot　hole　sizes,　leading　to　excessive　tool　changes　and　unnecessary　tool　movement,　which　limits　their　effectiveness,　preventing　them　from　fully　maximizing　efficiency　in　practical　production　settings.　To　tackle　this　challenge,　we　establish　a　practical　model:　Multitool　drilling　path　optimization　with　decision　(MTdDPO),　which　allows　pilot　hole　sizes　to　be　selectable　rather　than　fixed.　To　solve　MTdDPO,　we　develop　a　novel　multiagent　reinforcement　learning　(MARL)　method,　named　MARL　for　MTdDPO　(MM).　In　MM,　we　creatively　treat　the　holes　requiring　pilot　predrilling　as　agents　and　integrate　a　specially　designed　social　attribute　(SA)　mechanism　that　equips　the　agents　with　the　ability　to　perceive　the　geometric　relationships　with　surrounding　agents,　thereby　enhancing　their　decision-making　capabilities.　Furthermore,　the　convergence　of　MM　is　analyzed　to　demonstrate　its　theoretical　feasibility.　Finally,　the　effectiveness,　superiority,　and　practicality　of　the　MM　approach,　along　with　the　soundness　of　the　SA　mechanism,　are　validated　through　performance　comparisons,　iteration,　and　learning　curve　analyses　against　several　state-of-the-art　methods　on　eight　real-world　workpieces　of　varying　scales.