  We investigate how reinforcement learning agents can learn to cooperate.
Drawing inspiration from human societies, in which successful coordination of
many individuals is often facilitated by hierarchical organisation, we
introduce Feudal Multi-agent Hierarchies (FMH). In this framework, a 'manager'
agent, which is tasked with maximising the environmentally-determined reward
function, learns to communicate subgoals to multiple, simultaneously-operating,
'worker' agents. Workers, which are rewarded for achieving managerial subgoals,
take concurrent actions in the world. We outline the structure of FMH and
demonstrate its potential for decentralised learning and control. We find that,
given an adequate set of subgoals from which to choose, FMH performs, and
particularly scales, substantially better than cooperative approaches that use
a shared reward function.
