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A neural model of hierarchical reinforcement learning

Daniel Rasmussen, Aaron Voelker, Chris Eliasmith, Gennady Cymbalyuk
2017 PLoS ONE  
We present the first model capable of performing hierarchical reinforcement learning in a general, neurally detailed implementation.  ...  We show that this model is able to learn a spatial pickup and delivery task more quickly than one without hierarchical abilities.  ...  Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, the Canadian Foundation for Innovation, and Ontario Innovation Trust  ... 
doi:10.1371/journal.pone.0180234 pmid:28683111 pmcid:PMC5500327 fatcat:3dzfenv2dfb3ngrnecm7q5zjma

A neural model of hierarchical reinforcement learning

Daniel Rasmussen, Chris Eliasmith
unpublished
We present the first model capable of performing hierarchical reinforcement learning in a general, neurally detailed implementation.  ...  We show that this model is able to learn a spatial pickup and delivery task more quickly than one without hierarchical abilities.  ...  Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, the Canadian Foundation for Innovation, and Ontario Innovation Trust  ... 
fatcat:jnb2d2f42rgnlnfj6f7wi3o7vy

UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title A neural model of hierarchical reinforcement learning Publication Date A neural model of hierarchical reinforcement learning

Daniel Rasmussen, Chris Eliasmith, Daniel Rasmussen, Chris Eliasmith
2014 Proceedings of the Annual Meeting of the Cognitive Science Society   unpublished
We present the first model capable of performing hierarchical reinforcement learning in a general, neurally detailed implementation.  ...  We show that this model is able to learn a spatial pickup and delivery task more quickly than one without hierarchical abilities.  ...  Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Research Chairs, the Canadian Foundation for Innovation, and Ontario Innovation Trust  ... 
fatcat:24sbzqjd3zborbp3gr7gshwjqi

Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

Michael J. Frank, David Badre
2011 Cerebral Cortex  
A neural circuit model simulates interacting corticostriatal circuits organized hierarchically.  ...  We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence  ...  Conflict of Interest: None declared.  ... 
doi:10.1093/cercor/bhr114 pmid:21693490 pmcid:PMC3278315 fatcat:lnirkrnsu5guvjj5sz4kn4a3fy

Mechanisms of Hierarchical Reinforcement Learning in Cortico-Striatal Circuits 2: Evidence from fMRI

D. Badre, M. J. Frank
2011 Cerebral Cortex  
Mechanisms of hierarchical reinforcement learning in corticostriatal circuits I: computational analysis. 22:509--526), we provide novel neural circuit and algorithmic models of hierarchical cognitive control  ...  Results validate key predictions of the models and provide evidence for an individual cortico--striatal circuit for reinforcement learning of hierarchical structure at a specific level of policy abstraction  ...  D'Esposito who were coauthors along with D.B. on the publication of the original hierarchical learning task reported in Badre et al. (2010) and who consented to our reanalysis of the fMRI data from that  ... 
doi:10.1093/cercor/bhr117 pmid:21693491 pmcid:PMC3278316 fatcat:6xila4hogrgmlcym6lnr3ktabq

Stacked Neural Networks Must Emulate Evolution's Hierarchical Complexity

Michael Lamport Commons
2008 World Futures : Journal of General Evolution  
Stacked neural networks based on the Model of Hierarchical Complexity could emulate evolution's actual learning processes and behavioral reinforcement.  ...  orders of hierarchical complexity.  ...  HIERARCHICAL STACKED COMPUTER NEURAL NETWORKS BASED ON COMMONS' MODEL Animals, including humans, pass through a series of ordered stages of development (see "Introduction to the Model of Hierarchical Complexity  ... 
doi:10.1080/02604020802301568 fatcat:zc4f3ioz3na4vcifjdid4gqpc4

Neural Arithmetic Expression Calculator [article]

Kaiyu Chen, Yihan Dong, Xipeng Qiu, Zitian Chen
2018 arXiv   pre-print
In this work, we regard the arithmetic expression calculation as a hierarchical reinforcement learning problem.  ...  With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models.  ...  Hierarchical Reinforcement Learning The first popular hierarchical reinforcement learning model may date back to the options framework [6] .  ... 
arXiv:1809.08590v1 fatcat:kcxr6ksbejeonlhkt2ns6zxp3u

Deep Bayesian Natural Language Processing

Jen-Tzung Chien
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts  
The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies.  ...  Acknowledgments This work was partially supported by the Ministry of Science and Technology, Taiwan, under MOST 108-2634-F-009-003.  ...  This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process , Chinese  ... 
doi:10.18653/v1/p19-4006 dblp:conf/acl/Chien19 fatcat:bj6qf6cpkffz3oxinswh5fy4ry

Towards Sample Efficient Reinforcement Learning

Yang Yu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Reinforcement learning is a major tool to realize intelligent agents that can be autonomously adaptive to the environment.  ...  With deep models, reinforcement learning has shown great potential in complex tasks such as playing games from pixels.  ...  One particular direction of abstraction in reinforcement learning is hierarchical reinforcement learning, which has been developed for decades.  ... 
doi:10.24963/ijcai.2018/820 dblp:conf/ijcai/Yu18 fatcat:rhoz76vu2jfr3gc2zufhzhtppq

Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework [article]

Yang Liu
2018 arXiv   pre-print
Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given  ...  The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture.  ...  It can simulate the middle-layer of hierarchical feature computing process based on deep learning, reinforcement learning and incremental learning.  ... 
arXiv:1805.01385v2 fatcat:7zj5oejdxrelbmokc37zhxex5y

Hierarchical Reinforcement Learning with Deep Nested Agents [article]

Marc Brittain, Peng Wei
2018 arXiv   pre-print
Deep hierarchical reinforcement learning has gained a lot of attention in recent years due to its ability to produce state-of-the-art results in challenging environments where non-hierarchical frameworks  ...  We introduce the Deep Nested Agent framework, which is a variant of deep hierarchical reinforcement learning where information from the main agent is propagated to the low level nested agent by incorporating  ...  In Section III, a review of the current state-of-the-art hierarchical reinforcement learning approaches will be introduced.  ... 
arXiv:1805.07008v1 fatcat:xr6ee2au3zhmhhix33rtd452ym

Computational and Robotic Models of the Hierarchical Organization of Behavior: An Overview [chapter]

Gianluca Baldassarre, Marco Mirolli
2013 Computational and Robotic Models of the Hierarchical Organization of Behavior  
This book reviews the state of the art in computational and robotic models of the hierarchical organisation of behaviour.  ...  Together, the contributions give a good coverage of the most important models, findings, and challenges of the field.  ...  Acknowledgments This paper and a large part of the effort that led to this book have been supported by the Project 'IM-CLeVeR -Intrinsically Motivated Cumulative Learning Ver-  ... 
doi:10.1007/978-3-642-39875-9_1 fatcat:cypx4acb5ng5na2rsw7u7uhw3e

Combining Modalities with Different Latencies for Optimal Motor Control

Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, Okihide Hikosaka
2008 Journal of Cognitive Neuroscience  
neural coding through the model-based analysis of decision making, Journal of Neuroscience, 2007, Vol. 27, pp. 8178-8180. • DOYA, K., Reinforcement learning: Computational theory and biological mechanisms  ...  reinforcement learning: Temporal abstraction based on MOSAIC model, Transactions of Institute of Electronics, Information and Communication Engineers, 2006, Vol.  ... 
doi:10.1162/jocn.2008.20133 pmid:18416676 pmcid:PMC2941160 fatcat:nhmnllws5zhlrlsu5wjff2xtsa

Hierarchical extreme learning machine based reinforcement learning for goal localization

Nouar AlDahoul, Zaw Zaw Htike, Rini Akmeliawati
2017 IOP Conference Series: Materials Science and Engineering  
Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model.  ...  This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input.  ...  This paper proposes a deep model that is based on Hierarchical Extreme learning machine [8] in a visual reinforcement learning task.  ... 
doi:10.1088/1757-899x/184/1/012055 fatcat:eqchyfaskrbsbo6dmtz253fl3u

From internal models toward metacognitive AI [article]

Mitsuo Kawato
2021 arXiv   pre-print
The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs.  ...  One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials.  ...  The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs.  ... 
arXiv:2109.12798v2 fatcat:7foykaimirblrcxg3anqjo2jfm
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