  In this paper, we consider the problem of large scale multi agent
reinforcement learning. Firstly, we studied the representation problem of the
pairwise value function to reduce the complexity of the interactions among
agents. Secondly, we adopt a l2-norm trick to ensure the trivial term of the
approximated value function is bounded. Thirdly, experimental results on battle
game demonstrate the effectiveness of the proposed approach.
