As　is　well　known,　satellite　resources　are　extremely　scarce　relative　to　observation　demands.　Consequently,　the　Earth　observation　satellite　(EOS)　scheduling　becomes　a　remarkable　problem　which　is　of　significant　importance.　As　an　NP-hard　problem,　It　is　difficult　to　get　an　optimal　solution.　Furthermore,　real-time　scheduling　makes　it　even　more　challenging　for　researchers.　Unfortunately,　although　fruitful　results　have　been　achieved　in　the　category　of　EOS　scheduling,　there　still　exist　a　number　of　obvious　limitations　and　drawbacks.　For　example,　the　response　speed　and　stability　are　always　limited　in　the　scheduling　of　urgent　tasks　that　appeared　stochastically.　To　overcome　this　obstacle,　a　reinforcement　learning　algorithm,　which　is　of　the　ability　to　make　a　fast　response　for　the　urgent　task　scheduling,　has　been　proposed　in　this　paper.　In　order　to　improve　scheduling　stability　and　reduce　computational　complexity,　hierarchical　architecture　with　two　layers　has　been　established　for　the　proposed　algorithm.　In　each　hierarchical　layer,　we　adopt　an　online　learning　paradigm　to　explore　a　scheduling　strategy　at　the　learning　phase.　According　to　the　algorithm,　the　satellite　takes　a　scheduling　action　when　urgent　tasks　arrive　randomly　according　to　a　certain　strategy.　The　environment　will　feedback　to　the　satellite　by　the　corresponding　rewards　of　the　actions　taken.　After　multiple　feedback,　the　satellite　will　select　the　action　that　can　obtain　the　greatest　benefit.　In　practical　space　applications,　the　satellite　can　employ　the　learned　strategy　to　operate　the　low　orbit　satellite　selection　and　observation　time　window　(OTW)　assignment　for　urgent　tasks　in　stochastic　scenarios,　which　realize　an　immediate　schedule　and　maximize　scheduling　stability　at　the　same　time.　Finally,　a　numerical　experiment　has　been　performed.　The　simulation　results　demonstrate　that,　compared　to　the　heuristic　m-WSITF　algorithm,　the　proposed　algorithm　possesses　significant　advantages　in　effectiveness　and　efficiency,　especially　in　response　speed　and　stability.