BabyAI++: Towards Grounded-Language Learning beyond Memorization

Tianshi Cao, Jingkang Wang, Yining Zhang, Sivabalan Manivasagam

Abstract:

Despite success in many real-world tasks (e.g., robotics), reinforcement learning
(RL) agents still learn from tabula rasa when facing new and dynamic scenarios.
By contrast, humans can offload this burden through textual descriptions. Although
recent works have shown the benefits of instructive texts in goal-conditioned RL,
few have studied whether descriptive texts help agents to generalize across dynamic
environments. To promote research in this direction, we introduce a new platform,
BabyAI++, to generate various dynamic environments along with corresponding
descriptive texts. Moreover, we benchmark several baselines inherited from the instruction following setting and develop a novel approach towards visually-grounded
language learning on our platform. Extensive experiments show strong evidence
that using descriptive texts improves the generalization of RL agents across environments with varied dynamics. Code for BabyAI++ platform and baselines are
available online: https://github.com/caotians1/BabyAIPlusPlus