A　new　control　algorithm　-　N.B.S.Game　QLearning　was　introduced　to　solve　the　cooperation　control　of　two　adjacent　intersections　in　this　paper.　N.B.S.Game　was　denoted　as　two-player　cooperation　game　with　Nash　Bargaining　Solution.　Based　on　Game　Q-learning　algorithm,　that　the　game　theory　was　combined　with　the　Q-learning　realized　by　BP　neural　network　and　the　game　solution　was　regarded　as　the　basis　of　taking　the　strategy　selecting　of　Q-learning,　the　N.B.S.Game　Q-learning　algorithm　was　just　put　forward.　Because　the　traffic　signal　cooperation　control　problem　for　two　adjacent　intersections　belonged　to　the　two-player　general　sum　cooperation　game　form,　the　Nash　bargaining　solution　method　was　applied　to　obtain　the　optimal　portfolio　strategy　to　ensure　the　maximization　of　the　overall　benefit.　The　simulation　result　by　Paramics　has　showed　the　control　performance　of　the　N.B.S.Game　Q-learning　algorithm　is　far　better　than　fixed　time　control　in　heavy　traffic　flow　condition　and　the　control　strategy　can　adapt　to　the　variable　traffic　environment.　©　2009　IEEE.