\dm_csml_event_details UCL ELLIS

Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning


Speaker

Sanjeevan Ahilan

Affiliation

UCL, Gatsby

Date

Friday, 08 March 2019

Time

13:00-14:00

Location

Zoom

Link

Roberts 421

Event series

DeepMind/ELLIS CSML Seminar Series

Abstract

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.

Biography