  In this work, our goal is to train agents that can coordinate with seen,
unseen as well as human partners in a multi-agent communication environment
involving natural language. Previous work using a single set of agents has
shown great progress in generalizing to known partners, however it struggles
when coordinating with unfamiliar agents. To mitigate that, recent work
explored the use of population-based approaches, where multiple agents interact
with each other with the goal of learning more generic protocols. These
methods, while able to result in good coordination between unseen partners,
still only achieve so in cases of simple languages, thus failing to adapt to
human partners using natural language. We attribute this to the use of static
populations and instead propose a dynamic population-based meta-learning
approach that builds such a population in an iterative manner. We perform a
holistic evaluation of our method on two different referential games, and show
that our agents outperform all prior work when communicating with seen partners
and humans. Furthermore, we analyze the natural language generation skills of
our agents, where we find that our agents also outperform strong baselines.
Finally, we test the robustness of our agents when communicating with
out-of-population agents and carefully test the importance of each component of
our method through ablation studies.

Machine Learning;Artificial Intelligence;Computation and Language;Multiagent Systems;Machine Learning