Authors: Jiajun Xi, Yinong He, Jianing Yang, Yinpei Dai, Joyce Chai
Abstract: In real-world scenarios, it is desirable for embodied agents to have the
ability to leverage human language to gain explicit or implicit knowledge for
learning tasks. Despite recent progress, most previous approaches adopt simple
low-level instructions as language inputs, which may not reflect natural human
communication. It’s not clear how to incorporate rich language use to
facilitate task learning. To address this question, this paper studies
different types of language inputs in facilitating reinforcement learning (RL)
embodied agents. More specifically, we examine how different levels of language
informativeness (i.e., feedback on past behaviors and future guidance) and
diversity (i.e., variation of language expressions) impact agent learning and
inference. Our empirical results based on four RL benchmarks demonstrate that
agents trained with diverse and informative language feedback can achieve
enhanced generalization and fast adaptation to new tasks. These findings
highlight the pivotal role of language use in teaching embodied agents new
tasks in an open world. Project website:
https://github.com/sled-group/Teachable_RL
Source: http://arxiv.org/abs/2410.24218v1
