This　article　investigates　the　optimal　management　of　multi-carrier　water　and　energy　system　(MCWES)　considering　the　high　penetration　of　renewable　energy　sources　as　non-dispatchable　units　and　the　seawater　desalinization　mechanism　for　serving　water　demand　in　the　target　area.　The　proposed　model　encompasses　several　demand　layers　including　power　energy,　natural　gas　and　water　layer　which　supplies　the　electricity,　thermal　and　drinkable　water　demands　in　the　smart　island.　In　order　to　capture　the　uncertainty　effects　in　the　technical　decisions　of　optimal　scheduling,　a　stochastic　approach　based　on　unscented　transform　(UT)　is　developed　to　handle　the　forecast　error　in　the　electrical　and　thermal　energy　demands,　market　energy　prices　related　to　the　different　energy　layers　and　the　output　power　forecast　error　in　the　renewable　energy　sources.　Solving　the　proposed　coordinated　scheduling　problem　in　an　hourly　timescale　requires　heavy　calculations　that　make　it　impractical.　Therefore,　a　novel　reinforcement　learning　(RL)　based　approach　is　devised　for　finding　a　near　optimal　solution　and　facilitates　the　searching　process　with　a　trivial　computational　burden.　The　simulation　results　indicate　that　the　proposed　cooperation　approach　minimizes　both　the　operation　and　investment　costs　substantially　with　an　efficient　computational　burden　based　on　the　advanced　features　coming　out　of　the　proposed　RL　approach.　Last　but　not　least,　the　simulation　results　on　a　practical　smart　island　advocate　the　effectiveness　and　high　efficacy　of　the　proposed　model.　Also　it　was　seen　that　the　RL　approach　could　properly　solve　the　optimization　model　and　the　selection　of　the　sizes　of　the　components　was　highly　linked　to　the　hourly　values　of　demands　and　prices　of　the　energy.　©　2021　Elsevier　Ltd