Building agents capable of understanding language instructions is critical to effective and robust human-AI collaboration. Recent work focuses on training these instruction following agents via reinforcement learning in environments with synthetic language; however, these instructions often define long-horizon, sparse-reward tasks, and learning policies requires many episodes of experience. To this end, we introduce ELLA: Exploration through Learned Language Abstraction, a reward shaping approach that correlates high-level instructions with simpler low-level instructions to enrich the sparse rewards afforded by the environment. ELLA has two key elements: 1) A termination classifier that identifies when agents complete low-level instructions, and 2) A relevance classifier that correlates low-level instructions with success on high-level tasks. We learn the termination classifier offline from pairs of instructions and terminal states. Notably, in departure from prior work in language and abstraction, we learn the relevance classifier online, without relying on an explicit decomposition of high-level instructions to low-level instructions. On a suite of complex grid world environments with varying instruction complexities and reward sparsity, ELLA shows a significant gain in sample efficiency across several environments compared to competitive language-based reward shaping and no-shaping methods.

ELLA是一种基于奖励塑形的方法，在语言指令与简单低级组成件之间建立相关性，以提高稀疏奖励环境下机器人智能体的样本效率。

ELLA: 通过学习语言抽象进行探索