In　this　study,　a　novel　reinforcement　learning　task　supervisor　(RLTS)　with　memory　in　a　behavioral　control　framework　is　proposed　for　human-multi-robot　coordination　systems　(HMRCSs).　Existing　HMRCSs　suffer　from　high　decision-making　time　cost　and　large　task　tracking　errors　caused　by　repeated　human　intervention,　which　restricts　the　autonomy　of　multi-robot　systems　(MRSs).　Moreover,　existing　task　supervisors　in　the　null-space-based　behavioral　control　(NSBC)　framework　need　to　formulate　many　priority-switching　rules　manually,　which　makes　it　difficult　to　realize　an　optimal　behavioral　priority　adjustment　strategy　in　the　case　of　multiple　robots　and　multiple　tasks.　The　proposed　RLTS　with　memory　provides　a　detailed　integration　of　the　deep　Q-network　(DQN)　and　long　short-term　memory　(LSTM)　knowledge　base　within　the　NSBC　framework,　to　achieve　an　optimal　behavioral　priority　adjustment　strategy　in　the　presence　of　task　conflict　and　to　reduce　the　frequency　of　human　intervention.　Specifically,　the　proposed　RLTS　with　memory　begins　by　memorizing　human　intervention　history　when　the　robot　systems　are　not　confident　in　emergencies,　and　then　reloads　the　history　information　when　encountering　the　same　situation　that　has　been　tackled　by　humans　previously.　Simulation　results　demonstrate　the　effectiveness　of　the　proposed　RLTS.　Finally,　an　experiment　using　a　group　of　mobile　robots　subject　to　external　noise　and　disturbances　validates　the　effectiveness　of　the　proposed　RLTS　with　memory　in　uncertain　real-world　environments.