Effluent　scheduling　of　wastewater　treatment　process　(WWTP)　is　essential　to　ensure　compliance　with　regulatory　standards　regarding　effluent　quality.　Through　the　integration　of　pipe　and　plant　systems,　the　influent　can　be　estimated　prior　to　entering　the　treatment　process,　providing　additional　information　for　scheduling.　However,　the　traditional　evolutionary　computation　methods　face　challenges　in　utilizing　information　from　inflow　estimation,　resulting　in　decisions　that　do　not　account　for　long-term　returns.　For　solving　effluent　scheduling　problems　with　influent　estimation,　reinforcement　learning　can　facilitate　decision-making　based　on　long-term　environmental　factors　to　improve　the　optimization　ability　of　evolutionary　computations.　Thus,　a　framework　of　reinforcement　learning-assisted　particle　swarm　optimization　algorithm　(RLA-PSO)　is　proposed,　using　reinforcement　learning　part　to　generate　solutions　and　guide　optimization　by　learning　from　the　influent　estimation　on　a　long-time　scale.　Meanwhile,　it　employs　the　optimization　part　to　find　the　optimal　solutions　to　intensify　the　learning　effect　of　the　reinforcement　learning　part.　For　the　reinforcement　learning　part,　a　deep　Q-network　method　with　appropriate　states　and　rewards　is　designed　to　efficiently　learn　the　relationship　between　state,　action　and　reward　for　the　coming　period.　For　the　optimization　part,　a　set-based　particle　optimization　algorithm　is　employed　to　search　for　the　optimized　solution　in　a　future　period.　The　benchmark　simulation　model　No.1(BSM1)　is　used　to　evaluate　the　performance　of　the　proposed　RLA-PSO　algorithm　for　the　effluent　scheduling　problem　of　WWTP.　The　computational　experiments　to　the　state-of-the-art　methods　show　the　proposed　algorithm　can　achieve　superior　performance　in　effluent　quality　and　process　efficiency.　©　2025