Solving multiagent assignment Markov decision processes

We consider the setting of multiple collaborative agents trying to complete a set of tasks as assigned by a centralized controller. We propose a scalable method called“Assignmentbased decomposition” which is based on decomposing the problem of action selection into an upper assignment level and a lower task execution level. The assignment problem is solved by search, while the task execution is solved through coordinated reinforcement learning. We show that this decomposition of the overall problem into two levels scales well and outperforms the state-of-the-art approaches including pure assignment-level search or pure coordinated reinforcement learning. We also show how this approach enables transfer learning from domains with few agents to domains with many agents.