The central theme of this review is the dynamic interaction between information selection
and learning. We pose a fundamental question about this interaction: How do we learn what …

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

Despite continual debate for the past 30 years about the function of anterior cingulate cortex
(ACC), its key contribution to neurocognition remains unknown. However, recent …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

The hippocampus is critical for recollecting and imagining experiences. This is believed to
involve voluntarily drawing from hippocampal memory representations of people, events …

We propose a novel policy update that combines regularized policy optimization with model
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …

Primates can richly parse sensory inputs to infer latent information. This ability is
hypothesized to rely on establishing mental models of the external world and running mental …

Model-based planning is often thought to be necessary for deep, careful reasoning and
generalization in artificial agents. While recent successes of model-based reinforcement …

Credit assignment in reinforcement learning is the problem of measuring an action's
influence on future rewards. In particular, this requires separating skill from luck, ie …