This　paper　focuses　on　the　application　of　reinforcement　learning　to　obstacle　avoidance　in　dynamic　environments.　Behavior-based　control　architecture　is　more　robust　and　better　in　real-time　performance　than　conventional　model　based　architecture　in　the　control　of　mobile　robot.　An　intelligent　controller　is　proposed　by　integrating　reinforcement　learning　with　the　behavior-based　control　architecture　and　applied　to　the　obstacle　avoidance.　Neural　network　is　used　to　approximate　the　Q-function　to　store　the　Q-value.　By　using　the　reinforcement　learning,　the　mobile　robot　can　learn　to　select　proper　behavior　online　without　knowing　the　exact　model　of　the　system.　In　experiments,　dynamic　and　static　obstacles　are　placed　in　the　environments　separately.　Experiment　results　show　that　the　mobile　robot　can　get　to　the　target　point　without　colliding　with　any　obstacle　after　a　period　of　learning.