ArticlePloS one2017
A neural model of hierarchical reinforcement learning.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Hierarchical Reinforcement Learning, Sequential Behavior, and the Dorsal Frontostriatal System.Journal of cognitive neuroscience · 2022Review
- Spiking Neural Networks and Their Applications: A Review.Brain sciences · 2022Review
- Eye movements reveal spatiotemporal dynamics of visually-informed planning in navigation.eLife · 2022Article
- Neuromorphic Engineering Needs Closed-Loop Benchmarks.Frontiers in neuroscience · 2022Review
- Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application.Annals of translational medicine · 2021Review
- Discovery of hierarchical representations for efficient planning.PLoS computational biology · 2020Article
- NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods.Neuroinformatics · 2019Article
- Autonomous Functional Movements in a Tendon-Driven Limb via Limited Experience.Nature machine intelligence · 2019Article
- Feature-specific prediction errors and surprise across macaque fronto-striatal circuits.Nature communications · 2019Article
- A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents.Frontiers in computational neuroscience · 2019Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.