The　main　point　of　this　paper　is　to　provide　an　affirmative　answer　through　exploiting　reinforcement　learning　(RL)　in　artificial　intelligence　(AI)　for　eliminating　herding　without　any　external　control　in　complex　resource　allocation　systems.　In　particular,　we　demonstrate　that　when　agents　are　empowered　with　RL　(e.g.,　the　popular　Q-learning　algorithm　in　AI)　in　that　they　get　familiar　with　the　unknown　game　environment　gradually　and　attempt　to　deliver　the　optimal　actions　to　maximize　the　payoff,　herding　can　effectively　be　eliminated.　Furthermore,　computations　reveal　the　striking　phenomenon　that,　regardless　of　the　initial　state,　the　system　evolves　persistently　and　relentlessly　toward　the　optimal　state　in　which　all　resources　are　used　efficiently.　However,　the　evolution　process　is　not　without　interruptions:　there　are　large　fluctuations　that　occur　but　only　intermittently　in　time.　The　statistical　distribution　of　the　time　between　two　successive　fluctuating　events　is　found　to　depend　on　the　parity　of　the　evolution,　i.e.,　whether　the　number　of　time　steps　in　between　is　odd　or　even.　We　develop　a　physical　analysis　and　derive　mean-field　equations　to　gain　an　understanding　of　these　phenomena.　Since　AI　is　becoming　increasingly　widespread,　we　expect　our　RL　empowered　minority　game　system　to　have　broad　applications.