Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation. In Malthusian RL, increases in a subpopulation's average return drive subsequent increases in its size, just as Thomas Malthus argued in 1798 was the relationship between preindustrial income levels and population growth. Malthusian reinforcement learning harnesses the competitive pressures arising from growing and shrinking population size to drive agents to explore regions of state and policy spaces that they could not otherwise reach. Furthermore, in environments where there are potential gains from specialization and division of labor, we show that Malthusian reinforcement learning is better positioned to take advantage of such synergies than algorithms based on self-play.
@article{arxiv.1812.07019,
title = {Malthusian Reinforcement Learning},
author = {Joel Z. Leibo and Julien Perolat and Edward Hughes and Steven Wheelwright and Adam H. Marblestone and Edgar Duéñez-Guzmán and Peter Sunehag and Iain Dunning and Thore Graepel},
journal= {arXiv preprint arXiv:1812.07019},
year = {2019}
}