Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization

arXiv:2403.10967v1 Announce Type: new
Abstract: Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating generally capable embodied agents. To address the broader challenge, we start with the simpler setting of contextual reinforcement learning (cRL), assuming observability of the context values that parameterize the variation in the system's dynamics, such as the mass or dimensions of a robot, without making further simplifying assumptions about the observability of the Markovian state. Toward the goal of ZSG to unseen variation …

arxiv cs.ai cs.lg dreaming type world world models zero-shot