Plasticity refers to a network's ability to adapt to changing data distributions, which is crucial for the successful training of deep reinforcement learning agents. Loss of plasticity causes performance plateaus and contributes to scaling failures, overestimation bias, and insufficient exploration. To deepen the understanding of plasticity loss, we propose a unified definition, examine its drivers and pathologies, and organize over 50 mitigation strategies into the first comprehensive taxonomy of the field. Our analysis shows gaps in current evaluation practices and reveals that general regularization techniques often outperform domain-specific interventions. Future research should prioritize understanding the mechanisms underlying plasticity loss.
@article{arxiv.2411.04832,
title = {Plasticity Loss in Deep Reinforcement Learning: A Survey},
author = {Timo Klein and Christoph Luther and Manus McAuliffe and Lukas Miklautz and Claudia Plant and Sebastian Tschiatschek},
journal= {arXiv preprint arXiv:2411.04832},
year = {2026}
}