Animal learning is associated with changes in the efficacy of connections between neurons.
The rules that govern this plasticity can be tested in neural networks. Rules that train neural …

For both humans and machines, the essence of learning is to pinpoint which components in
its information processing pipeline are responsible for an error in its output, a challenge that …

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the
processing of fast yet content-poor feedback information to correct assumptions about the …

Changes of synaptic connections between neurons are thought to be the physiological basis
of learning. These changes can be gated by neuromodulators that encode the presence of …

A central problem in cognitive neuroscience is how animals can manage to rapidly master
complex sensorimotor tasks when the only sensory feedback they use to improve their …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …

The ability to autonomously learn behaviors via direct interactions in uninstrumented
environments can lead to generalist robots capable of enhancing productivity or providing …

Reinforcement learning algorithms have provided some of the most influential computational
theories for behavioral learning that depends on reward and penalty. After briefly reviewing …

Reinforcement learning (RL) algorithms provide powerful explanations for simple learning
and decision-making behaviors and the functions of their underlying neural substrates …

Reinforcement learning is the process by which an agent learns to predict long-term future
reward. We now understand a great deal about the brain's reinforcement learning …