Before revision:  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 protocoland 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;
After revision:  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 protocoland employ it to train agents in two challenging request-fulfilling problems demonstrate the strengths of our algorithm: compared to RL baselines, it is more sample-efficient;