As　the　largest　Internet-of-Things　(IoT)　deployment　in　the　world,　the　smart　grid　implements　extremely　reduction　in　the　energy　dissipation　for　the　operation　of　the　smart　city.　However,　the　electricity　data　produced　by　the　smart　grid　contain　massive　sensitive　information,　such　as　dispatching　instructions　and　bills.　The　data　are　always　revealed　to　cloud　servers　in　the　plaintext　format　for　the　$Q$　-learning-based　energy　strategy　making,　which　gives　the　chance　for　the　adversary　to　abuse　the　user　data.　Therefore,　in　this　article,　we　propose　a　lightweight　privacy-preserving　$Q$　-learning　framework　(LiPSG)　for　the　energy　management　strategy　making　of　the　smart　grid.　Before　being　sent　to　the　control　center,　the　electricity　data　of　each　power　supply　region　in　LiPSG　are　first　split　into　uniformly　random　secret　shares.　During　completion　of　the　computation　task　of　$Q$　-learning,　the　data　are　kept　in　the　random　share　format　all　the　time　to　avoid　the　data　privacy　disclosure.　The　computation　feature　is　implemented　by　the　newly　proposed　additive　secret-sharing　protocols.　The　edge　computing　technology　is　also　deployed　to　further　improve　efficiency.　Moreover,　comprehensive　theoretic　analysis　and　experiments　are　given　to　prove　the　security　and　efficiency　of　LiPSG.　Compared　with　the　existing　privacy-preserving　schemes　of　the　smart　grid,　LiPSG　first　provides　a　general　$Q$　-learning-based　privacy-preserving　power　strategy　making　architecture　with　high　efficiency　and　low-performance　loss.