Cascaded Attention: Adaptive and Gated Graph Attention Network for Multiagent Reinforcement Learning.

Modeling the interactive relationships of agents is critical to improving the collaborative capability of a multiagent system. Some methods model these by predefined rules. However, due to the nonstationary problem, the interactive relationship changes over time and cannot be well captured by rules. Other methods adopt a simple mechanism such as an attention network to select the neighbors the current agent should collaborate with. However, in large-scale multiagent systems, collaborative relationships are too complicated to be described by a simple attention network. We propose an adaptive and gated graph attention network (AGGAT), which models the interactive relationships between agents in a cascaded manner. In the AGGAT, we first propose a graph-based hard attention network that roughly filters irrelevant agents. Then, normal soft attention is adopted to decide the importance of each neighbor. Finally, gated attention further refines the collaborative relationship of agents. By using cascaded attention, the collaborative relationship of agents is precisely learned in a coarse-to-fine style. Extensive experiments are conducted on a variety of cooperative tasks. The results indicate that our proposed method outperforms state-of-the-art baselines.