  In this paper, we study the cooperative Multi-Agent Reinforcement Learning
(MARL) problems using Reward Machines (RMs) to specify the reward functions
such that the prior knowledge of high-level events in a task can be leveraged
to facilitate the learning efficiency. Unlike the existing work that RMs have
been incorporated into MARL for task decomposition and policy learning in
relatively simple domains or with an assumption of independencies among the
agents, we present Multi-Agent Reinforcement Learning with a Hierarchy of RMs
(MAHRM) that is capable of dealing with more complex scenarios when the events
among agents can occur concurrently and the agents are highly interdependent.
  MAHRM exploits the relationship of high-level events to decompose a task into
a hierarchy of simpler subtasks that are assigned to a small group of agents,
so as to reduce the overall computational complexity.
  Experimental results in three cooperative MARL domains show that MAHRM
outperforms other MARL methods using the same prior knowledge of high-level
events.
