It　is　necessary　for　China　to　accelerate　the　low-carbon　power　transition　to　realize　the　carbon　neutrality　target　by　2060.　Based　on　the　goal　of　carbon　neutrality,　Deep　Reinforcement　Learning　with　the　Double　Q-learning　method　is　used　to　solve　a　multi-agent　game　in　30　provinces　of　China,　in　an　attempt　to　explore　the　evolutionary　process　of　regional　low-carbon　power　transition　strategies,　carbon　emissions,　and　carbon　emission　decoupling　with　economic　growth.　Conclusions　are　obtained　as　follows.　First,　regional　low-carbon　power　transition　strategies　are　not　consistent.　Though　China　is　bound　to　achieve　the　carbon　peak　and　carbon　neutrality,　the　carbon　peak　and　carbon　neutrality　process　will　vary　widely　across　regions　and　over　time.　Second,　the　cost　of　power　decarbonization　is　higher　in　resource-based　regions　than　that　in　core　regions.　The　regional　coordination　policy　can　reduce　the　total　carbon　emissions　in　resource-based　regions,　but　it　will　not　alleviate　their　carbon　emission　decoupling　costs.　Third,　the　stable　strategy　of　low-carbon　power　transition　needs　faster　economic　development　and　more　abundant　zero-carbon　resources.　Therefore,　the　carbon　neutrality　policy　is　suggested　to　be　more　spatially　differentiated,　more　equitable,　and　more　robust.