The　obstacles　avoidance　of　manipulator　is　a　hot　issue　in　the　field　of　robot　control.　Artificial　Potential　Field　Method　(APFM)　is　a　widely　used　obstacles　avoidance　path　planning　method,　which　has　prominent　advantages.　However,　APFM　also　has　some　shortcomings,　which　include　the　inefficiency　of　avoiding　obstacles　close　to　target　or　dynamic　obstacles.　In　view　of　the　shortcomings　of　APFM,　Reinforcement　Learning　(RL)　only　needs　an　automatic　learning　model　to　continuously　improve　itself　in　the　specified　environment,　which　makes　it　capable　of　optimizing　APFM　theoretically.　In　this　paper,　we　introduce　an　approach　hybridizing　RL　and　APFM　to　solve　those　problems.　We　define　the　concepts　of　Distance　reinforcement　factors　(DRF)　and　Force　reinforcement　factors　(FRF)　to　make　RL　and　APFM　integrated　more　effectively.　We　disassemble　the　reward　function　of　RL　into　two　parts　through　DRF　and　FRF,　and　make　them　activate　in　different　situations　to　optimize　APFM.　Our　method　can　obtain　better　obstacles　avoidance　performance　through　finding　the　optimal　strategy　by　RL,　and　the　effectiveness　of　the　proposed　algorithm　is　verified　by　multiple　sets　of　simulation　experiments,　comparative　experiments　and　physical　experiments　in　different　types　of　obstacles.　Our　approach　is　superior　to　traditional　APFM　and　the　other　improved　APFM　method　in　avoiding　collisions　and　approaching　obstacles　avoidance.　At　the　same　time,　physical　experiments　verify　the　practicality　of　the　proposed　algorithm.