Aiming　at　the　problem　that　the　multi-robot　task　allocation　method　in　soccer　system　is　easy　to　fall　into　the　problem　of　local　optimal　solution　and　real-time　performance,　a　new　multi-robot　task　allocation　method　is　proposed.　First　of　all,　in　order　to　improve　the　speed　and　efficiency　of　finding　optimal　actions　and　make　better　use　of　the　disadvantages　that　traditional　Q-learning　can＇t　often　propagate　negative　values,　we　propose　a　new　way　to　propagate　negative　values,　that　is,　Q-learning　methods　based　on　negative　rewards.　Next,　in　order　to　adapt　to　the　dynamic　external　environment,　an　adaptive　greedy　method　of　which　the　mode　of　operation　is　judged　by　the　value　is　proposed.　This　method　is　based　on　the　classical　-greedy.　In　the　process　of　solving　problems,　can　be　adaptively　changed　as　needed　for　a　better　balance　of　exploration　and　exploitation　in　reinforcement　learning.　Finally,　we　apply　this　method　to　the　robot＇s　football　game　system.　It　has　been　experimentally　proven　that　dangerous　actions　can　be　avoided　effectively　by　the　Q-learning　method　which　can　spread　negative　rewards.　The　adaptive　-greedy　strategy　can　be　used　to　adapt　to　the　external　environment　better　and　faster　so　as　to　improve　the　speed　of　convergence.　Copyright　©　2019　Inderscience　Enterprises　Ltd.