Google Research

The State of Sparse Training in Deep Reinforcement Learning

Proceedings of the 39th International Conference on Machine Learning, PMLR (2022)


The use of sparse neural networks has seen a rapid growth in recent years, particularly in computer vision; their appeal stems largely due to the reduced number of parameters required to train and store, as well as in an increase in learning efficiency. Somewhat surprisingly, there have been very few efforts exploring their use in deep reinforcement learning (DRL). In this work we perform a systematic investigation into applying a number of existing sparse training techniques on a variety of DRL agents and environments. Our results highlight the overall challenge that reinforcement learning poses for sparse training methods, complemented by detailed analyses on how the various components in DRL are affected by the use of sparse networks. We conclude by suggesting some promising avenues for improving the effectiveness of general sparse training methods, as well as for advancing their use in DRL.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work