The Emergence of Grammar through Reinforcement Learning
On the map: Deep reinforcement learning methods → RL training of language and agent models
Abstract (arXiv)
The evolution of grammatical systems of syntactic and semantic composition is modeled here with a novel application of reinforcement learning theory. To test the functionalist thesis that speakers' expressive purposes shape their language, we include within the model a probability distribution over different messages that could be expressed in a given context. The proposed learning and production algorithm then breaks down language learning into a sequence of simple steps, such that each step benefits from the message probabilities. The results are presented in the form of numerical simulations of language histories and analytic proofs. The potential for applying these mathematical models to the study of natural language is illustrated with two case studies from the history of English.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 0 of its 65 references that are in the map. First posted on arXiv 3 Mar 2025.
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