Traffic lights control could be regarded as a multi-agent coordinated problem. A model-free reinforcement learning (RL) approach is a powerful framework for solving such coordinated policy-making problems without prior environmental knowledge. In order to approach a global policy, communication among agents needs to be built. To enable dynamic and scalable communication, we propose a new RL model, CommNet based on Local Attention Mechanism (Attn-CommNet), which uses local selection and attention mechanism between hidden layers to facilitate cooperation. We evaluated the proposed method using synthetic and real word traffic flows under multi-scale road networks. The results demonstrate that the proposed method can get better performance in multi-scale problems, especially large-scale problems compared to the state-of-the-art methods.