  Plasticity, the ability of a neural network to evolve with new data, is
crucial for high-performance and sample-efficient visual reinforcement learning
(VRL). Although methods like resetting and regularization can potentially
mitigate plasticity loss, the influences of various components within the VRL
framework on the agent's plasticity are still poorly understood. In this work,
we conduct a systematic empirical exploration focusing on three primary
underexplored facets and derive the following insightful conclusions: (1) data
augmentation is essential in maintaining plasticity; (2) the critic's
plasticity loss serves as the principal bottleneck impeding efficient training;
and (3) without timely intervention to recover critic's plasticity in the early
stages, its loss becomes catastrophic. These insights suggest a novel strategy
to address the high replay ratio (RR) dilemma, where exacerbated plasticity
loss hinders the potential improvements of sample efficiency brought by
increased reuse frequency. Rather than setting a static RR for the entire
training process, we propose Adaptive RR, which dynamically adjusts the RR
based on the critic's plasticity level. Extensive evaluations indicate that
Adaptive RR not only avoids catastrophic plasticity loss in the early stages
but also benefits from more frequent reuse in later phases, resulting in
superior sample efficiency.
