To handle this problem, a natural approach is to increase the data diversity by image based augmentations. However, different with most vision tasks such as classification and detection, RL tasks are not always invariant to spatial based augmentations duetotheentanglement ofenvironment dynamics andvisual appearance.

Deep reinforcement learning (RL) is computationally demanding and requiresprocessing of many data points. Synchronous methods enjoy training stability while having lowerdatathroughput.

Whilemuchwork has focused on different weight pruning criteria, the overallsparsifiabilityofthe network, i.e., its capacity to be pruned without quality loss, has often been overlooked.