A　multi-agent　reinforcement　learning　mission　supervisor　(MARLMS)　is　designed　for　differential　drive　robots　using　trial-and-error　learning.　The　proposed　MARLMS　addresses　the　challenge　inherent　in　behavior-based　multi-agent　systems,　wherein　the　design　of　switching　rules　to　determine　behavior　priorities　relies　heavily　on　human　intelligence.　Building　upon　the　null-space-based　behavioral　control　(NSBC)　framework,　a　differential　model　is　introduced　to　replace　the　particle　model.　Consequently,　a　paradigm　of　NSBC　with　nonholonomic　constraints　is　presented　for　the　first　time,　enhancing　the　system　robustness　to　the　minimum　extremum　state.　Subsequently,　a　joint　policy　is　developed　to　dynamically　and　intelligently　determine　behavior　priorities　by　modeling　the　behavior　priority　switching　problem　as　a　cooperative　Markov　game.　The　proposed　MARLMS　not　only　eliminates　the　need　for　manual　design　of　switching　rules　but　also　reduces　the　computational　and　storage　burdens　during　online　operations.　Simulation　results　demonstrate　the　superior　behavior　priority　switching　performance　of　the　proposed　MARLMS.　Furthermore,　successful　implementation　on　AgileX　Limo　robots　validates　the　practicality　of　the　proposed　MARLMS.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.