Abstract

We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (lc-rl) approaches are great tools in this quest, as they allow to express abstract goals as sets of constraints on the states. However, most lc-rl agents are not autonomous and cannot learn without external instructions and feedback. Besides, their direct language condition cannot account for the goal-directed behavior of pre-verbal infants and strongly limits the expression of behavioral diversity for a given language input. To resolve these issues, we propose a new conceptual approach to language-conditioned rl: the Language-Goal-Behavior architecture (lgb). lgb decouples skill learning and language grounding via an intermediate semantic representation of the world. To showcase the properties of lgb, we present a specific implementation called decstr . decstr is an intrinsically motivated learning agent endowed with an innate semantic representation describing spatial relations between physical objects. In a first stage (g $\to$ b), it freely explores its environment and targets self-generated semantic configurations. In a second stage (l $\to$ g), it trains a la