What technique is specifically designed for continuous learning scenarios?

Gradient Episodic Memory is designed specifically for continuous learning scenarios by allowing a model to retain and recall previously learned knowledge while still being able to incorporate new information. This technique addresses the challenge of catastrophic forgetting, which occurs when a model forgets previously acquired knowledge upon learning new tasks. By using episodic memory, the model can store important memories from past tasks and utilize them when faced with new tasks, ensuring that it maintains performance across a range of experiences over time.

The other methods, while valuable, do not focus specifically on the continuous learning aspect. Reinforcement learning typically involves learning optimal actions based on rewards received from actions taken in an environment, but it does not inherently address the retention of prior learning in a sequential task setting. Transfer learning involves applying knowledge gained in one context to a different but related context, which may not directly support continuous learning. Multi-task learning involves training a model on multiple tasks simultaneously, but it also does not directly tackle the challenge of maintaining prior knowledge when new tasks are introduced.