Training multi-agent AI systems to solve complex tasks through cooperation

A novel approach to cooperative multi-agent reinforcement learning (RL) that assigns tasks to individual agents within a group, thereby improving the entire group's ability to collaborate. We tested this method in the real-time strategy game StarCraft: Brood War, and found that our RL-trained model significantly outperformed computer-controlled players that relied on carefully tuned rule-based baselines. Perhaps most important, these gains carried over to matches with significantly larger armies than what we included in our training scenarios. We're releasing the source code for this approach on our TorchCraftAI GitHub repository, and detailing our results, which indicate that treating collaborative multi-agent RL as a dynamic assignment problem can lead to groups of agents that are better at generalizing to more complex situations. Our approach focuses on multi-agent collaborative (MAC) problems where agents have to carry out multiple intermediate tasks in order to accomplish a larger one.