Continual learning agents experience a stream of (related) tasks. The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream. We are interested in the intersection of two recent continual-learning scenarios. In meta-continual learning, the model is pre-trained using meta-learning to minimize catastrophic forgetting of previous tasks. In continual-meta learning, the aim is to train agents for faster remembering of previous tasks through adaptation.

For readability, we omit OSAKA pre-training. Replay-based methods store representative samples from the past, either in their original form (e.g., rehearsal Most prior-based methods rely on task boundaries. Since non-stationary data distributions breaks the i.i.d assumption for The update is computed from a parametric combination of the gradient of the current and previous task. Despite that, meta-continual learning is actively researched [61, 6]. Bayesian change-point detection scheme to identify whether a task has changed.

Continual learning agents experience a stream of (related) tasks.

Continual learning agents experience a stream of (related) tasks.

Weaknesses: My main concern with the submission is that the evaluation scenario OSAKA seems too specific and designed primarily for a set of algorithms in between Meta- & Continual-Learning while failing to make a broader argument for other approaches to Continual Learning. While certain aspects of OSAKA are certainly desirable (OOD tasks, Unknown task changes, Online Evaluation) there is a strong assumption made in allowing for Pre-training that may not be adequate in certain CL settings, limiting the generality of OSAKA. Furthermore, it is unclear how aspects such as controllable non-stationarity would be implemented in Reinforcement Learning. Furthermore, I personally feel that if task-revisiting is to be implemented, new OOD tasks should be designed in a way that explicitly re-uses skills that can be learned on a previous problem in a novel setting, instead of merely re-visiting the problem without modification. The problem with this assumption in general is that Catastrophic Forgetting is significantly reduced through an implicit form of replay provided by the environment, making it difficult to tell to which extent catastrophic forgetting is actually a problem of these algorithms.

Continual learning agents experience a stream of (related) tasks. The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream. We are interested in the intersection of two recent continual-learning scenarios. In meta-continual learning, the model is pre-trained using meta-learning to minimize catastrophic forgetting of previous tasks. In continual-meta learning, the aim is to train agents for faster remembering of previous tasks through adaptation.

Sharp's production base in Sakai, Osaka Prefecture, once renowned as the world's most advanced factory for large LCD television panels, will be transformed into a massive data center to handle the enormous volume of data for artificial intelligence applications, according to recent announcements by Japan's two major mobile carriers. SoftBank announced on Friday that it has reached a basic agreement with Sharp to construct an AI data center at the Sakai plant site. Additionally, telecom giant KDDI revealed last week that it is in talks with Sharp to create a similar data center. Struggling to make its LCD business profitable, Sharp previously announced it would end production at the Sakai plant by September.