Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural
  Architectures for Real-Time Strategy Games
  In real-time strategy (RTS) game artificial intelligence research, various
multi-agent deep reinforcement learning (MADRL) algorithms are widely and
actively used nowadays. Most of the research is based on StarCraft II
environment because it is the most well-known RTS games in world-wide. In our
proposed MADRL-based algorithm, distributed MADRL is fundamentally used that is
called QMIX. In addition to QMIX-based distributed computation, we consider
state categorization which can reduce computational complexity significantly.
Furthermore, self-attention mechanisms are used for identifying the
relationship among agents in the form of graphs. Based on these approaches, we
propose a categorized state graph attention policy (CSGA-policy). As observed
in the performance evaluation of our proposed CSGA-policy with the most
well-known StarCraft II simulation environment, our proposed algorithm works
well in various settings, as expected.
