Th State of Spars Train ng in D ep Re nforc m nt Le rn ng
We perform a systematic investigation into applying a number of existing sparse training techniques on a variety of deep RL agents and environments, and conclude by suggesting promising avenues for improving the effectiveness of sparse training methods, as well as for advancing their use in DRL.
Laura Graesser*, Utku Evci*, Erich Elsen, Pablo Samuel Castro
This blogpost is a summary of our ICML 2022 paper. The code is available here. Many more results and analyses are available in the paper, so I encouraged you to check it out if interested!