In factored Markov decision process (FMDP) such as Robocup system, the effect to value evaluation of various states is different from each other within state attributes. There are some important state attributes that can determine the whole state value either uniquely, or at least, approximately. Instead of using the relevance among states to reduce the state space, this paper addresses the problem of curse of dimensionality in large FMDP by approximating state value function through feature vector extraction. A key contribution of this paper is that it reduces the computation complexity by constraints reduction in linear programming, speeds up the production of joint strategy by transplanting the value function to the more complex game in reinforcement learning. Experimental results are provided on Robocup free kick, demonstrating a promising indication of the efficiency of the approach and its’ ability of transplanting the learning result. Comparing this algorithm to an existing state-of-the-art approach indicates that it can not only improve the learning speed, but also can transplant state value function to the Robocup with more players instead of learning again.