title: From Grunts to Grammar: Emergent Language from Cooperative Foraging
summary: 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.
paper_authors._id: 682c51889f83963d2d41998c
paper_authors.name: Maytus Piriyajitakonkij
paper_authors._id: 682c51889f83963d2d41998d
paper_authors.name: Rujikorn Charakorn
paper_authors._id: 682c51889f83963d2d41998e
paper_authors.name: Weicheng Tao
paper_authors._id: 682c51889f83963d2d41998f
paper_authors.name: Wei Pan
paper_authors._id: 682c51889f83963d2d419990
paper_authors.name: Mingfei Sun
paper_authors._id: 682c51889f83963d2d419991
paper_authors.name: Cheston Tan
paper_authors._id: 682c51889f83963d2d419992
paper_authors.name: Mengmi Zhang
paper_title: From Grunts to Grammar: Emergent Language from Cooperative Foraging
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: deep reinforcement learning communication protocols arbitrariness interchangeability displacement cultural transmission compositionality