  This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm
for pruning neural networks. Unlike current RL based model compression
approaches where feedback is given only at the end of each episode to the
agent, PuRL provides rewards at every pruning step. This enables PuRL to
achieve sparsity and accuracy comparable to current state-of-the-art methods,
while having a much shorter training cycle. PuRL achieves more than 80%
sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75.37% on
the ImageNet dataset. Through our experiments we show that PuRL is also able to
sparsify already efficient architectures like MobileNet-V2. In addition to
performance characterisation experiments, we also provide a discussion and
analysis of the various RL design choices that went into the tuning of the
Markov Decision Process underlying PuRL. Lastly, we point out that PuRL is
simple to use and can be easily adapted for various architectures.
