Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

Modularity is a compelling solution to continual learning (CL), the problem of modeling
sequences of related tasks. Learning and then composing modules to solve different tasks …

Biological neural networks are systems of extraordinary computational capabilities shaped
by evolution, development, and lifelong learning. The interplay of these elements leads to …

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution,
artificial neural networks are trained through evolutionary algorithms, taking inspiration from …

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 …

Incremental learning is one of the most important abilities of human beings. In the age of
artificial intelligence, it is the key task to make neural network models as powerful as human …

Continual learning studies agents that learn from streams of tasks without forgetting previous
ones while adapting to new ones. Two recent continual-learning scenarios have opened …

Recent theoretical and experimental works have connected Hebbian plasticity with the
reinforcement learning (RL) paradigm, producing a class of trial-and-error learning in …

Photonic devices, in comparison to their conventional electronic counterparts, are gaining
prominence as essential constituents of an increasing array of devices and applications …