Before revision:  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 . The resulting masked observation space eliminates redundant information and yields minimal task-relevant representations. The mask identified by iterative magnitude pruning provides an interpretable inductive bias.
After revision:  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 rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning provides an interpretable inductive bias.