Understanding the World to Solve Social Dilemmas Using Multi-Agent
  Reinforcement Learning
Manuel Rios Nicanor Quijano Luis Felipe Giraldo
Social dilemmas are situations where groups of individuals can benefit from
mutual cooperation but conflicting interests impede them from doing so. This
type of situations resembles many of humanity's most critical challenges, and
discovering mechanisms that facilitate the emergence of cooperative behaviors
is still an open problem. In this paper, we study the behavior of
self-interested rational agents that learn world models in a multi-agent
reinforcement learning (RL) setting and that coexist in environments where
social dilemmas can arise. Our simulation results show that groups of agents
endowed with world models outperform all the other tested ones when dealing
with scenarios where social dilemmas can arise. We exploit the world model
architecture to qualitatively assess the learnt dynamics and confirm that each
agent's world model is capable to encode information of the behavior of the
changing environment and the other agent's actions. This is the first work that
shows that world models facilitate the emergence of complex coordinated
behaviors that enable interacting agents to ``understand'' both environmental
and social dynamics.