From Grunts to Grammar: Emergent Language from Cooperative Foraging
Early cavemen relied on gestures, vocalizations, and simple signals to
coordinate, plan, avoid predators, and share resources. Today, humans
collaborate using complex languages to achieve remarkable results. What drives
this evolution in communication? How does language emerge, adapt, and become
vital for teamwork? Understanding the origins of language remains a challenge.
A leading hypothesis in linguistics and anthropology posits that language
evolved to meet the ecological and social demands of early human cooperation.
Language did not arise in isolation, but through shared survival goals.
Inspired by this view, we investigate the emergence of language in multi-agent
Foraging Games. These environments are designed to reflect the cognitive and
ecological constraints believed to have influenced the evolution of
communication. Agents operate in a shared grid world with only partial
knowledge about other agents and the environment, and must coordinate to
complete games like picking up high-value targets or executing temporally
ordered actions. Using end-to-end deep reinforcement learning, agents learn
both actions and communication strategies from scratch. We find that agents
develop communication protocols with hallmark features of natural language:
arbitrariness, interchangeability, displacement, cultural transmission, and
compositionality. We quantify each property and analyze how different factors,
such as population size and temporal dependencies, shape specific aspects of
the emergent language. Our framework serves as a platform for studying how
language can evolve from partial observability, temporal reasoning, and
cooperative goals in embodied multi-agent settings. We will release all data,
code, and models publicly.
682c51889f83963d2d41998c
Maytus Piriyajitakonkij
682c51889f83963d2d41998d
Rujikorn Charakorn
claimed_verified
2025-06-23T08:32:43.712Z
676be0014210747726a9afa4
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/UKRZ--twSm870IcKtK9ve.png
Charakorn
user
Rujikorn
682c51889f83963d2d41998e
Weicheng Tao
682c51889f83963d2d41998f
Wei Pan
682c51889f83963d2d419990
Mingfei Sun
682c51889f83963d2d419991
Cheston Tan
682c51889f83963d2d419992
Mengmi Zhang
From Grunts to Grammar: Emergent Language from Cooperative Foraging
Early cavemen relied on gestures, vocalizations, and simple signals to
coordinate, plan, avoid predators, and share resources. Today, humans
collaborate using complex languages to achieve remarkable results. What drives
this evolution in communication? How does language emerge, adapt, and become
vital for teamwork? Understanding the origins of language remains a challenge.
A leading hypothesis in linguistics and anthropology posits that language
evolved to meet the ecological and social demands of early human cooperation.
Language did not arise in isolation, but through shared survival goals.
Inspired by this view, we investigate the emergence of language in multi-agent
Foraging Games. These environments are designed to reflect the cognitive and
ecological constraints believed to have influenced the evolution of
communication. Agents operate in a shared grid world with only partial
knowledge about other agents and the environment, and must coordinate to
complete games like picking up high-value targets or executing temporally
ordered actions. Using end-to-end deep reinforcement learning, agents learn
both actions and communication strategies from scratch. We find that agents
develop communication protocols with hallmark features of natural language:
arbitrariness, interchangeability, displacement, cultural transmission, and
compositionality. We quantify each property and analyze how different factors,
such as population size and temporal dependencies, shape specific aspects of
the emergent language. Our framework serves as a platform for studying how
language can evolve from partial observability, temporal reasoning, and
cooperative goals in embodied multi-agent settings. We will release all data,
code, and models publicly.
Agents in multi-agent Foraging Games using deep reinforcement learning develop communication strategies with properties akin to natural language due to cooperation and shared goals.
deep reinforcement learning communication protocols arbitrariness interchangeability displacement cultural transmission compositionality