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 …

Much of recent machine learning has focused on deep learning, in which neural network
weights are trained through variants of stochastic gradient descent. An alternative approach …

Evolution provides a creative fount of complex and subtle adaptations that often surprise the
scientists who discover them. However, the creativity of evolution is not limited to the natural …

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 …

Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …

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

A long-standing goal in artificial intelligence is creating agents that can learn a variety of
different skills for different problems. In the artificial intelligence subfield of neural networks …

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in
synaptic connectivity. Importantly, these changes are not passive, but are actively controlled …

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize
controllers for real autonomous robots based only on a task specification. While a number of …