Hole-making　is　one　of　the　basic　processes　of　mechanical　production.　For　the　problem　of　toolpath　optimization　of　computer　numerical　control　(CNC)　machine　tools,　a　novel　toolpath　model　for　hole-making　called　as　multi-tool　drilling　path　optimization　problems　with　decidable　holes　(MTdDPO)　is　proposed.　In　the　MTdDPO,　holes　on　workpieces　are　divided　into　two　categories:　fixed　holes　and　decidable　holes.　The　goal　of　the　MTdDPO　is　to　minimize　the　length　of　the　machining　path　by　judging　the　path　ownership　of　decidable　holes　and　the　machining　sequence　of　all　holes　in　each　path.　To　realize　the　optimization　of　the　MTdDPO,　a　segmented　genetic　algorithm　based　on　reinforcement　learning　(RLSGA)　is　proposed.　The　population　of　the　RLSGA　is　regarded　as　the　agent,　the　states　of　the　agent　are　the　intervals　of　the　diversity　coefficient　of　the　population,　three　different　segmental　crossover　operators　are　the　actions　of　the　agent,　and　the　reward　of　the　agent　is　related　to　the　changes　in　fitness　value　and　diversity　coefficients　of　the　population.　Based　on　the　MTdDPO,　5　benchmark　test　problems　are　designed,　and　the　RLSGA　is　compared　with　other　4　algorithms　on　these　test　problems.　Results　show　that　the　performance　of　the　RLSGA　is　significantly　better　than　other　algorithms,　which　means　the　RLSGA　can　effectively　solve　the　MTdDPO　problems.　©　2024　Northeast　University.　All　rights　reserved.