A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

We demonstrate that plasticity loss is pervasive under domain shift in this regime, and that a number of methods developed to resolve it in other settings fail, sometimes even performing worse than applying no intervention at all. In contrast, we find that a class of "regenerative" methods are able to consistently mitigate plasticity loss in a variety of contexts, including in gridworld tasks and