Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use

TL;DR

This paper investigates how the informativeness and diversity of language inputs affect the learning and generalization of embodied reinforcement learning agents, demonstrating that richer language use improves adaptability and performance.

Contribution

It provides a systematic study of different language input types, highlighting the importance of diverse and informative language in teaching embodied RL agents.

Findings

Diverse language feedback enhances agent generalization.

Informative language accelerates learning and adaptation.

Rich language use improves performance on RL benchmarks.

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