Before revision:  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 ). We argue that policies pre-trained to maximize coverage will produce behavior thatis useful for both strategies. When using these policies for both exploitation and exploration, our agents discover better solutions  .
After revision:  The behavior of pre-trained policies for exploration and that is complementary to transferring neural network weights. Our experiments show that, when combined with large-scale pre-training in the absence of rewards, existing intrinsic motivation objectives can lead to the emergence of complex behaviors. These pre-trained policies can then be leveraged by BT to discover better solutions .