  In real applications of Reinforcement Learning (RL), such as robotics, low
latency and energy efficient inference is very desired. The use of sparsity and
pruning for optimizing Neural Network inference, and particularly to improve
energy and latency efficiency, is a standard technique. In this work, we
perform a systematic investigation of applying these optimization techniques
for different RL algorithms in different RL environments, yielding up to a
400-fold reduction in the size of neural networks.
