Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning
Abstract
In this paper, we formulate the challenge of re-conceptualising the language game experimental paradigm in the framework of multi-agent reinforcement learning (MARL). If successful, future language game experiments will benefit from the rapid and promising methodological advances in the MARL community, while future MARL experiments on learning emergent communication will benefit from the insights and results gained from language game experiments. We strongly believe that this cross-pollination has the potential to lead to major breakthroughs in the modelling of how human-like languages can emerge and evolve in multi-agent systems.
Keywords
Cite
@article{arxiv.2004.04722,
title = {Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning},
author = {Paul Van Eecke and Katrien Beuls},
journal= {arXiv preprint arXiv:2004.04722},
year = {2020}
}
Comments
This paper was accepted for presentation at the 2020 AAAI Spring Symposium `Challenges and Opportunities for Multi-Agent Reinforcement Learning' after a double-blind reviewing process