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.
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) , the description feedback is richer and allows for improved sample complexity . 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.