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 .
After revision:  In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity .