  We present a novel interactive learning protocol that enables training
request-fulfilling agents by verbally describing their activities. Unlike
imitation learning (IL), our protocol allows the teaching agent to provide
feedback in a language that is most appropriate for them. Compared with reward
in reinforcement learning (RL), the description feedback is richer and allows
for improved sample complexity. We develop a probabilistic framework and an
algorithm that practically implements our protocol. Empirical results in two
challenging request-fulfilling problems demonstrate the strengths of our
approach: compared with RL baselines, it is more sample-efficient; compared
with IL baselines, it achieves competitive success rates without requiring the
teaching agent to be able to demonstrate the desired behavior using the
learning agent's actions. Apart from empirical evaluation, we also provide
theoretical guarantees for our algorithm under certain assumptions about the
teacher and the environment.
