Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning

Understand

Autonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions.

• They must discover potential goals, learn their own reward functions and engage in their own learning trajectory.
• Children, however, benefit from exposure to language, helping to organize and mediate their thought.
• We propose LE2 (Language Enhanced Exploration), a learning algorithm leveraging intrinsic motivations and natural language (NL) interactions with a descriptive social partner (SP).