Plasticity Loss

Plasticity loss, the decreasing ability of neural networks to adapt to new information during training, is a significant challenge in continual learning, particularly within reinforcement learning (RL) settings. Current research focuses on understanding the underlying mechanisms of plasticity loss, including its relationship to loss landscape sharpness and neural collapse, and developing mitigation strategies such as regenerative methods, normalization techniques, and learned optimization algorithms. Overcoming plasticity loss is crucial for improving the robustness and adaptability of AI systems in dynamic environments, with implications for various applications ranging from robotics to personalized medicine.