Aiming　at　agent＇s　behavioral　cognition　problem,　a　behavior　cognition　computational　model　based　on　the　coordination　of　cerebellum　and　basal　ganglia　is　proposed.　Operant　conditioning　learning　algorithm　is　the　central　algorithm　including　evaluation　mechanism,　action　selection　mechanism,　tropism　mechanism,　and　the　coordination　mechanism　between　cerebellum　and　basal　ganglia.　The　learning　signals　come　from　not　only　the　Inferior　Olive　but　also　the　Substantia　Nigra　in　the　beginning.　The　convergence　of　the　algorithm　can　be　guaranteed　in　the　sense　of　entropy.　With　the　proposed　method,　a　motor　nerve　cognitive　system　for　the　self-balancing　two-wheeled　robot　has　been　built　using　the　RBF　neural　network　as　the　actor　and　evaluation　function　approximator.　The　simulation　results　show　that　the　learning　speed　is　increased　as　well　as　the　failure　times　are　reduced　by　the　proposed　method　than　by　the　Actor-Critic　method　with　the　only　Basal　Ganglia　mechanism.　Through　decreasing　temperature　in　the　late　stage,　the　learning　speed　is　increased　and　the　vibration　disappeares　eventually,　and　the　learning　effect　is　improved.