Many failures in deep continual and reinforcement learning are associated with increasing
magnitudes of the weights, making them hard to change and potentially causing overfitting …

Deep representation learning methods struggle with continual learning, suffering from both
catastrophic forgetting of useful units and loss of plasticity, often due to rigid and unuseful …

Deep neural networks provide Reinforcement Learning (RL) powerful function
approximators to address large-scale decision-making problems. However, these …

Modern deep policy gradient methods achieve effective performance on simulated robotic
tasks, but they all require large replay buffers or expensive batch updates, or both, making …

Catastrophic forgetting remains a significant challenge to continual learning for decades.
While recent works have proposed effective methods to mitigate this problem, they mainly …