Before revision:  In this work, we  show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity . Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
After revision:  In this work, we show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity , suggesting that the distributional shift has a limited impact on the size of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .