Tell me why! Explanations support learning relational and causal structure. (arXiv:2112.03753v3 [cs.LG] UPDATED)

Inferring the abstract relational and causal structure of the world is a
major challenge for reinforcement-learning (RL) agents. For humans,
language--particularly in the form of explanations--plays a considerable role
in overcoming this challenge. Here, we show that language can play a similar
role for deep RL agents in complex environments. While agents typically
struggle to acquire relational and causal knowledge, augmenting their
experience by training them to predict language descriptions and explanations
can overcome these limitations. We show that language …

arxiv learning support