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Abstract

We investigate how reinforcement learning agents can learn tocooperate. Drawing inspiration from human societies, in whichsuccessful coordination of many individuals is often facilitated byhierarchical organisation, we introduce Feudal Multi-agent Hierar-chies (FMH). In this framework, a ‘manager’ agent, which is taskedwith maximising the environmentally-determined reward func-tion, learns to communicate subgoals to multiple, simultaneously-operating, ‘worker’ agents. Workers, which are rewarded for achiev-ing managerial subgoals, take concurrent actions in the world. Weoutline the structure of FMH and demonstrate its potential for de-centralised learning and control. We find that, given an adequate setof subgoals from which to choose, FMH performs, and particularlyscales, substantially better than cooperative approaches that use ashared reward function.