Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

The effective representation, processing, analysis, and visualization of large-scale structured
data, especially those related to complex domains, such as networks and graphs, are one of …

Many reinforcement learning (RL) tasks provide the agent with high-dimensional
observations that can be simplified into low-dimensional continuous states. To formalize this …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

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 …

Transfer in reinforcement learning refers to the notion that generalization should occur not
only within a task but also across tasks. We propose a transfer framework for the scenario …

Task scheduling, which plays a vital role in cloud computing, is a critical factor that
determines the performance of cloud computing. From the booming economy of information …

We review the psychology and neuroscience of reinforcement learning (RL), which has
experienced significant progress in the past two decades, enabled by the comprehensive …