Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Computational psychiatry: towards a mathematically informed understanding of mental illness
Computational Psychiatry aims to describe the relationship between the brain's
neurobiology, its environment and mental symptoms in computational terms. In so doing, it …
neurobiology, its environment and mental symptoms in computational terms. In so doing, it …
Hierarchical control over effortful behavior by rodent medial frontal cortex: A computational model.
CB Holroyd, SM McClure - Psychological review, 2015 - psycnet.apa.org
The anterior cingulate cortex (ACC) has been the focus of intense research interest in recent
years. Although separate theories relate ACC function variously to conflict monitoring …
years. Although separate theories relate ACC function variously to conflict monitoring …
The interpretation of computational model parameters depends on the context
Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences,
promising to explain behavior from simple conditioning to complex problem solving, to shed …
promising to explain behavior from simple conditioning to complex problem solving, to shed …
Computational evidence for hierarchically structured reinforcement learning in humans
MK Eckstein, AGE Collins - Proceedings of the National …, 2020 - National Acad Sciences
Humans have the fascinating ability to achieve goals in a complex and constantly changing
world, still surpassing modern machine-learning algorithms in terms of flexibility and …
world, still surpassing modern machine-learning algorithms in terms of flexibility and …
What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience
Highlights•'Reinforcement learning'(RL) refers to different concepts in machine learning,
psychology, and neuroscience.•In psychology and neuroscience, RL models have provided …
psychology, and neuroscience.•In psychology and neuroscience, RL models have provided …
Metis: Learning to schedule long-running applications in shared container clusters at scale
Online cloud services are increasingly deployed as long-running applications (LRAs) in
containers. Placing LRA containers is known to be difficult as they often have sophisticated …
containers. Placing LRA containers is known to be difficult as they often have sophisticated …
Learning representations in model-free hierarchical reinforcement learning
Abstract Common approaches to Reinforcement Learning (RL) are seriously challenged by
large-scale applications involving huge state spaces and sparse delayed reward feedback …
large-scale applications involving huge state spaces and sparse delayed reward feedback …
Temporal and state abstractions for efficient learning, transfer, and composition in humans.
L Xia, AGE Collins - Psychological review, 2021 - psycnet.apa.org
Humans use prior knowledge to efficiently solve novel tasks, but how they structure past
knowledge during learning to enable such fast generalization is not well understood. We …
knowledge during learning to enable such fast generalization is not well understood. We …
A neural model of hierarchical reinforcement learning
We develop a novel, biologically detailed neural model of reinforcement learning (RL)
processes in the brain. This model incorporates a broad range of biological features that …
processes in the brain. This model incorporates a broad range of biological features that …