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In this paper, we consider the problem of large scale multi agentreinforcement learning. Firstly, we studied the representation problem of thepairwise value function to reduce the complexity of the interactions amongagents. Secondly, we adopt a l2-norm trick to ensure the trivial term of theapproximated value function is bounded. Thirdly, experimental results on battlegame demonstrate the effectiveness of the proposed approach.

Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning