Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning

TL;DR

Findings indicate that certain linguistic structures more effectively improve numerical comprehension in RL agents, and the model predicts optimal sequences for presenting numbers to RL agents which enhance their speed of learning.

Abstract

This paper investigates how children learn numbers using the framework of reinforcement learning (RL), with a focus on the impact of language instructions. The motivation for using reinforcement learning stems from its parallels with psychological learning theories in controlled environments. By using state of the art deep reinforcement learning models, we simulate and analyze the effects of various forms of language instructions on number acquisition. Our findings indicate that certain linguistic structures more effectively improve numerical comprehension in RL agents. Additionally, our model predicts optimal sequences for presenting numbers to RL agents which enhance their speed of learning. This research provides valuable insights into the interplay between language and numerical cognition, with implications for both educational strategies and the development of artificial intelligence systems designed to support early childhood learning.

Figures (14)

• Figure 1: Visual information provided by the environment to the agent.
• Figure 2: Policy based instruction sequence.
• Figure 3: State based instruction sequence.
• Figure 4: Model 1: Visual only model.
• Figure 5: Model 2: Pre-trained visual and language model