  The ability to learn continually is essential in a complex and changing
world. In this paper, we characterize the behavior of canonical value-based
deep reinforcement learning (RL) approaches under varying degrees of
non-stationarity. In particular, we demonstrate that deep RL agents lose their
ability to learn good policies when they cycle through a sequence of Atari 2600
games. This phenomenon is alluded to in prior work under various guises --
e.g., loss of plasticity, implicit under-parameterization, primacy bias, and
capacity loss. We investigate this phenomenon closely at scale and analyze how
the weights, gradients, and activations change over time in several experiments
with varying dimensions (e.g., similarity between games, number of games,
number of frames per game), with some experiments spanning 50 days and 2
billion environment interactions. Our analysis shows that the activation
footprint of the network becomes sparser, contributing to the diminishing
gradients. We investigate a remarkably simple mitigation strategy --
Concatenated ReLUs (CReLUs) activation function -- and demonstrate its
effectiveness in facilitating continual learning in a changing environment.
