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 …

Memory and planning rely on learning the structure of relationships among experiences.
Compact representations of these structures guide flexible behavior in humans and animals …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

How is it that thoroughly physical material beings such as ourselves can think, dream, feel,
create and understand ideas, theories and concepts? How does mere matter give rise to all …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

A cognitive map has long been the dominant metaphor for hippocampal function, embracing
the idea that place cells encode a geometric representation of space. However, evidence for …

Theories of reward learning in neuroscience have focused on two families of algorithms
thought to capture deliberative versus habitual choice.'Model-based'algorithms compute the …

Humans and animals are capable of evaluating actions by considering their long-run future
rewards through a process described using model-based reinforcement learning (RL) …

Learning robust value functions given raw observations and rewards is now possible with
model-free and model-based deep reinforcement learning algorithms. There is a third …

The observation of place cells has suggested that the hippocampus plays a special role in
encoding spatial information. However, place cell responses are modulated by several …