Animals　achieve　precise　locomotion　through　learning　and　practice,　and　many　scholars　are　investigating　how　animals　acquire　new　skills.　Deep　Reinforcement　Learning　(DRL)　is　often　employed　in　skill　learning　and　locomotion　in　robotics.　However,　the　relationship　between　the　learning　process　and　neural　networks　or　DRL　remains　unclear.　In　this　paper,　we　propose　a　framework　of　backpropagation　(BP)　network　based　on　matrix　multiplication,　which　is　implemented　and　visualized　on　a　Field　Programmable　Gate　Array　(FPGA).　This　allows　us　to　track　changes　in　each　neuron　by　analyzing　nonlinear　signals.　By　considering　each　register　in　the　FPGA　as　a　neuron,　we　can　plot　the　weights　of　joints,　which　provides　insights　into　the　learning　rules.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.