Before revision:  In supervised settings, representations discovered through unsupervised pre-training offer important benefits when transferred to downstream tasks . Given the nature of the reinforcement learning problem, we argue that representation alone is not enough for efficient transfer in challenging domains and explore how to transfer knowledge through behavior. The behavior of pre-trained policies may be used for solving the task at hand (exploitation), as well as for collecting useful data to solve the problem (exploration ).
After revision:  In supervised settings, representations discovered through unsupervised pre-training offer important benefits when transferred to downstream tasks . Given the nature of the reinforcement learning problem, we argue that representation alone is not enough for efficient transfer in challenging domains . We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies may be used for solving the task at hand (exploitation), as well as for collecting useful data to solve the problem (exploration ).