Considering　the　intricate　traffic　flow　dynamics　between　the　mainline　and　entrance　ramp　areas　of　highways,　a　ramp　control　model　based　on　deep　reinforcement　learning　(DRL)　is　proposed.　Leveraging　the　characteristics　of　traffic　flow,　this　study　builds　upon　existing　reinforcement　learning　(RL)　ramp　control　models　and　optimizes　the　design　of　the　state　space,　action　space,　and　reward　function.　Utilizing　the　PyCharm　platform,　SUMO　simulation　software　is　adopted　for　secondary　development.　Simulation　experiments　are　conducted　in　various　scenarios　involving　different　congestion　durations　and　demand　distributions.　Under　identical　road　network　conditions,　four　distinct　control　methods　are　devised:　the　no-control　(NC)　scenario,　the　ALINEA　ramp　control　method,　the　RL　control　method,　and　the　proposed　DRL　method.　The　experimental　outcomes　indicate　that,　compared　to　the　NC　method,　all　three　alternative　methods　substantially　reduce　the　average　travel　time.　The　proposed　DRL　method　exhibits　an　average　travel　time　reduction　of　12.77%　compared　to　the　ALINEA　method　and　a　5.22%　reduction　compared　to　the　RL　method.　Furthermore,　the　proposed　DRL　method　achieves　a　reduction　of　approximately　39.01%　in　the　ramp　queue　length　compared　to　the　ALINEA　method.　©　2023　IEEE.