Behavior-based　autonomous　systems　rely　on　human　intelligence　to　resolve　multi-mission　conflicts　by　designing　mission　priority　rules　and　nonlinear　controllers.　In　this　work,　a　novel　two-layer　reinforcement　learning　behavioral　control　(RLBC)　method　is　proposed　to　reduce　such　dependence　by　trial-and-error　learning.　Specifically,　in　the　upper　layer,　a　reinforcement　learning　mission　supervisor　(RLMS)　is　designed　to　learn　the　optimal　mission　priority.　Compared　with　existing　mission　supervisors,　the　RLMS　improves　the　dynamic　performance　of　mission　priority　adjustment　by　maximizing　cumulative　rewards　and　reducing　hardware　storage　demand　when　using　neural　networks.　In　the　lower　layer,　a　reinforcement　learning　controller　(RLC)　is　designed　to　learn　the　optimal　control　policy.　Compared　with　existing　behavioral　controllers,　the　RLC　reduces　the　control　cost　of　mission　priority　adjustment　by　balancing　control　performance　and　consumption.　All　error　signals　are　proved　to　be　semi-globally　uniformly　ultimately　bounded　(SGUUB).　Simulation　results　show　that　the　number　of　mission　priority　adjustment　and　the　control　cost　are　significantly　reduced　compared　to　some　existing　mission　supervisors　and　behavioral　controllers,　respectively.