In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Learning continuously during all model lifetime is fundamental to deploy machine learning
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …

The staple of human intelligence is the capability of acquiring knowledge in a continuous
fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, the sub …

Catastrophic forgetting occurs when a neural network loses the information learned in a
previous task after training on subsequent tasks. This problem remains a hurdle for artificial …

The reviews had two major concerns: lack of a benchmarking on a complex dataset, and
unclear writing. To address these two major issues we: 1-Rewrote experiments section with …

Computers have undergone tremendous improvements in performance over the last 60
years, but those improvements have significantly slowed down over the last decade, owing …

Continual lifelong learning requires an agent or model to learn many sequentially ordered
tasks, building on previous knowledge without catastrophically forgetting it. Much work has …

Damage to the hippocampal system disrupts recent memory but leaves remote memory
intact. The account presented here suggests that memories are first stored via synaptic …

All natural cognitive systems, and, in particular, our own, gradually forget previously learned
information. Plausible models of human cognition should therefore exhibit similar patterns of …

Both regular inflectional patterns (walk-walked) and irregular ones (swing-swung) can be
applied productively to novel words (eg wug-wugged; spling-splung). Theories of generative …