  We present a novel interactive learning protocol that enables training
request-fulfilling agents by verbally describing their activities. Our protocol
gives rise to a new family of interactive learning algorithms that offer
complementary advantages against traditional algorithms like imitation learning
(IL) and reinforcement learning (RL). We develop an algorithm that practically
implements this protocol and employ it to train agents in two challenging
request-fulfilling problems using purely language-description feedback.
Empirical results demonstrate the strengths of our algorithm: compared to RL
baselines, it is more sample-efficient; compared to IL baselines, it achieves
competitive success rates while not requiring feedback providers to have
agent-specific expertise. We also provide theoretical guarantees of the
algorithm under certain assumptions on the teacher and the environment.
