Generalized　matrix　multiplication　(GEMM)　has　become　the　fundamental　operator　in　deep　learning.　Operators　of　deep　learning　need　to　be　transformed　and　optimized　into　executable　code　for　specific　hardware.　However,　existing　deep　learning　frameworks　rely　on　handwritten　libraries　that　are　not　scalable　for　various　hardware　target.　In　this　paper　we　introduce　a　compiler-level　tuning　algorithm　based　on　Neighborhood　Actor　Advantage　Critic　(N-A2C)　method　for　GEMM　tiling　optimization.　Experiment　results　shows　that　our　method　achieves　8%　and　20%　computational　time　savings　of　GEMM　compared　Xgboost　and　RNN　methods　when　exploring　1%　of　the　search　space.　©　2024　ACM.