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You searched for subject:(Bayesian reinforcement learning). Showing records 1 – 30 of 32 total matches.

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University of Alberta

1. Gendron-Bellemare, Marc. Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains.

Degree: PhD, Department of Computing Science, 2013, University of Alberta

 This thesis presents new algorithms for dealing with large scale reinforcement learning problems. Central to this work is the Atari 2600 platform, which acts as… (more)

Subjects/Keywords: Bayesian model learning; Atari 2600; Reinforcement learning

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APA (6th Edition):

Gendron-Bellemare, M. (2013). Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/fn106z868

Chicago Manual of Style (16th Edition):

Gendron-Bellemare, Marc. “Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains.” 2013. Doctoral Dissertation, University of Alberta. Accessed July 04, 2020. https://era.library.ualberta.ca/files/fn106z868.

MLA Handbook (7th Edition):

Gendron-Bellemare, Marc. “Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains.” 2013. Web. 04 Jul 2020.

Vancouver:

Gendron-Bellemare M. Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains. [Internet] [Doctoral dissertation]. University of Alberta; 2013. [cited 2020 Jul 04]. Available from: https://era.library.ualberta.ca/files/fn106z868.

Council of Science Editors:

Gendron-Bellemare M. Fast, Scalable Algorithms for Reinforcement Learning in High Dimensional Domains. [Doctoral Dissertation]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/fn106z868

2. Dorfman, Hayley. Computational Mechanisms Underlying the Influence of Agency on Learning.

Degree: PhD, 2019, Harvard University

We live in an uncertain environment where making flexible predictions about the occurrence of positive and negative events is necessary for maximizing rewards, minimizing punishments,… (more)

Subjects/Keywords: agency; reinforcement learning; Bayesian; control; beliefs

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APA (6th Edition):

Dorfman, H. (2019). Computational Mechanisms Underlying the Influence of Agency on Learning. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029566

Chicago Manual of Style (16th Edition):

Dorfman, Hayley. “Computational Mechanisms Underlying the Influence of Agency on Learning.” 2019. Doctoral Dissertation, Harvard University. Accessed July 04, 2020. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029566.

MLA Handbook (7th Edition):

Dorfman, Hayley. “Computational Mechanisms Underlying the Influence of Agency on Learning.” 2019. Web. 04 Jul 2020.

Vancouver:

Dorfman H. Computational Mechanisms Underlying the Influence of Agency on Learning. [Internet] [Doctoral dissertation]. Harvard University; 2019. [cited 2020 Jul 04]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029566.

Council of Science Editors:

Dorfman H. Computational Mechanisms Underlying the Influence of Agency on Learning. [Doctoral Dissertation]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029566


University of Sydney

3. Morere, Philippe. Bayesian Optimisation for Planning And Reinforcement Learning .

Degree: 2019, University of Sydney

 This thesis addresses the problem of achieving efficient non-myopic decision making by explicitly balancing exploration and exploitation. Decision making, both in planning and reinforcement learning(more)

Subjects/Keywords: Reinforcement Learning; Exploration; Planning; POMDP; Bayesian; Uncertainity

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APA (6th Edition):

Morere, P. (2019). Bayesian Optimisation for Planning And Reinforcement Learning . (Thesis). University of Sydney. Retrieved from https://ses.library.usyd.edu.au/handle/2123/21230

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Morere, Philippe. “Bayesian Optimisation for Planning And Reinforcement Learning .” 2019. Thesis, University of Sydney. Accessed July 04, 2020. https://ses.library.usyd.edu.au/handle/2123/21230.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Morere, Philippe. “Bayesian Optimisation for Planning And Reinforcement Learning .” 2019. Web. 04 Jul 2020.

Vancouver:

Morere P. Bayesian Optimisation for Planning And Reinforcement Learning . [Internet] [Thesis]. University of Sydney; 2019. [cited 2020 Jul 04]. Available from: https://ses.library.usyd.edu.au/handle/2123/21230.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Morere P. Bayesian Optimisation for Planning And Reinforcement Learning . [Thesis]. University of Sydney; 2019. Available from: https://ses.library.usyd.edu.au/handle/2123/21230

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Ontario Institute of Technology

4. Coles, Rory. Using machine learning methods to aid scientists in laboratory environments.

Degree: 2019, University of Ontario Institute of Technology

 As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the… (more)

Subjects/Keywords: Machine learning; Deep learning; Neural networks; Reinforcement learning; Bayesian Modelling

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APA (6th Edition):

Coles, R. (2019). Using machine learning methods to aid scientists in laboratory environments. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1137

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Coles, Rory. “Using machine learning methods to aid scientists in laboratory environments.” 2019. Thesis, University of Ontario Institute of Technology. Accessed July 04, 2020. http://hdl.handle.net/10155/1137.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Coles, Rory. “Using machine learning methods to aid scientists in laboratory environments.” 2019. Web. 04 Jul 2020.

Vancouver:

Coles R. Using machine learning methods to aid scientists in laboratory environments. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10155/1137.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Coles R. Using machine learning methods to aid scientists in laboratory environments. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1137

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

5. Subramanian, Kaushik. Policy-based exploration for efficient reinforcement learning.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making tasks modeled as Markov Decision Processes. Researchers have shown RL to be… (more)

Subjects/Keywords: Reinforcement learning; Exploration; Interactive learning; Active learning; Monte-carlo search; Bayesian

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APA (6th Edition):

Subramanian, K. (2020). Policy-based exploration for efficient reinforcement learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62831

Chicago Manual of Style (16th Edition):

Subramanian, Kaushik. “Policy-based exploration for efficient reinforcement learning.” 2020. Doctoral Dissertation, Georgia Tech. Accessed July 04, 2020. http://hdl.handle.net/1853/62831.

MLA Handbook (7th Edition):

Subramanian, Kaushik. “Policy-based exploration for efficient reinforcement learning.” 2020. Web. 04 Jul 2020.

Vancouver:

Subramanian K. Policy-based exploration for efficient reinforcement learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1853/62831.

Council of Science Editors:

Subramanian K. Policy-based exploration for efficient reinforcement learning. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62831


Oregon State University

6. Wynkoop, Michael S. Learning MDP action models via discrete mixture trees.

Degree: MS, Computer Science, 2008, Oregon State University

 This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the… (more)

Subjects/Keywords: Dynamic Bayesian Network; Reinforcement learning (Machine learning)  – Mathematical models

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APA (6th Edition):

Wynkoop, M. S. (2008). Learning MDP action models via discrete mixture trees. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/9096

Chicago Manual of Style (16th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Masters Thesis, Oregon State University. Accessed July 04, 2020. http://hdl.handle.net/1957/9096.

MLA Handbook (7th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Web. 04 Jul 2020.

Vancouver:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1957/9096.

Council of Science Editors:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Masters Thesis]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/9096


Colorado State University

7. Lee, Minwoo. Sparse Bayesian reinforcement learning.

Degree: PhD, Computer Science, 2017, Colorado State University

 This dissertation presents knowledge acquisition and retention methods for efficient and robust learning. We propose a framework for learning and memorizing, and we examine how… (more)

Subjects/Keywords: continuous action space; practice; sparse learning; knowledge retention; Bayesian learning; reinforcement learning

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APA (6th Edition):

Lee, M. (2017). Sparse Bayesian reinforcement learning. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/183935

Chicago Manual of Style (16th Edition):

Lee, Minwoo. “Sparse Bayesian reinforcement learning.” 2017. Doctoral Dissertation, Colorado State University. Accessed July 04, 2020. http://hdl.handle.net/10217/183935.

MLA Handbook (7th Edition):

Lee, Minwoo. “Sparse Bayesian reinforcement learning.” 2017. Web. 04 Jul 2020.

Vancouver:

Lee M. Sparse Bayesian reinforcement learning. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10217/183935.

Council of Science Editors:

Lee M. Sparse Bayesian reinforcement learning. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/183935


Texas A&M University

8. Imani, Mahdi. Estimation, Inference and Learning of Partially-Observed Dynamical Systems.

Degree: PhD, Electrical Engineering, 2019, Texas A&M University

 Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need to learn how to ride safely by recognizing pedestrians, traffic… (more)

Subjects/Keywords: Dynamical Systems; Bayesian Optimization; Reinforcement Learning; Machine Learning; Inference; Dynamical Systems; Hidden Markov Model

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APA (6th Edition):

Imani, M. (2019). Estimation, Inference and Learning of Partially-Observed Dynamical Systems. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/184417

Chicago Manual of Style (16th Edition):

Imani, Mahdi. “Estimation, Inference and Learning of Partially-Observed Dynamical Systems.” 2019. Doctoral Dissertation, Texas A&M University. Accessed July 04, 2020. http://hdl.handle.net/1969.1/184417.

MLA Handbook (7th Edition):

Imani, Mahdi. “Estimation, Inference and Learning of Partially-Observed Dynamical Systems.” 2019. Web. 04 Jul 2020.

Vancouver:

Imani M. Estimation, Inference and Learning of Partially-Observed Dynamical Systems. [Internet] [Doctoral dissertation]. Texas A&M University; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1969.1/184417.

Council of Science Editors:

Imani M. Estimation, Inference and Learning of Partially-Observed Dynamical Systems. [Doctoral Dissertation]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/184417


University of Southern California

9. Kim, Sung Shin. Computational models and model-based fMRI studies in motor learning.

Degree: PhD, Neuroscience, 2014, University of Southern California

 In the last decade, computational models in motor learning have become popular because it provides a theoretical framework not only to explain but predict motor… (more)

Subjects/Keywords: fMRI; motor learning; spacing effects; contextual interference effects; multi-rate adaptation; reinforcement learning; Bayesian

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APA (6th Edition):

Kim, S. S. (2014). Computational models and model-based fMRI studies in motor learning. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/310639/rec/1551

Chicago Manual of Style (16th Edition):

Kim, Sung Shin. “Computational models and model-based fMRI studies in motor learning.” 2014. Doctoral Dissertation, University of Southern California. Accessed July 04, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/310639/rec/1551.

MLA Handbook (7th Edition):

Kim, Sung Shin. “Computational models and model-based fMRI studies in motor learning.” 2014. Web. 04 Jul 2020.

Vancouver:

Kim SS. Computational models and model-based fMRI studies in motor learning. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2020 Jul 04]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/310639/rec/1551.

Council of Science Editors:

Kim SS. Computational models and model-based fMRI studies in motor learning. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/310639/rec/1551


University of Georgia

10. Bhat, Sanath Govinda. Learning driver preferences for freeway merging using multitask irl.

Degree: MS, Computer Science, 2017, University of Georgia

 Most automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Hierarchical Bayesian Model; Multitask; Highway Merging; NGSIM; Likelihood Weighting

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APA (6th Edition):

Bhat, S. G. (2017). Learning driver preferences for freeway merging using multitask irl. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37273

Chicago Manual of Style (16th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Masters Thesis, University of Georgia. Accessed July 04, 2020. http://hdl.handle.net/10724/37273.

MLA Handbook (7th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Web. 04 Jul 2020.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10724/37273.

Council of Science Editors:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37273


University of Georgia

11. Bhat, Sanath Govinda. Learning driver preferences for freeway merging using multitask irl.

Degree: MS, Computer Science, 2017, University of Georgia

 Most automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Hierarchical Bayesian Model; Multitask; Highway Merging; NGSIM; Likelihood Weighting

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APA (6th Edition):

Bhat, S. G. (2017). Learning driver preferences for freeway merging using multitask irl. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37116

Chicago Manual of Style (16th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Masters Thesis, University of Georgia. Accessed July 04, 2020. http://hdl.handle.net/10724/37116.

MLA Handbook (7th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Web. 04 Jul 2020.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10724/37116.

Council of Science Editors:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37116


University of British Columbia

12. Cora, Vlad M. Model-based active learning in hierarchical policies .

Degree: 2008, University of British Columbia

 Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games.… (more)

Subjects/Keywords: Hierarchical Reinforcement Learning; Decision Theory; Bayesian Active Learning; Robotics

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APA (6th Edition):

Cora, V. M. (2008). Model-based active learning in hierarchical policies . (Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/737

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Cora, Vlad M. “Model-based active learning in hierarchical policies .” 2008. Thesis, University of British Columbia. Accessed July 04, 2020. http://hdl.handle.net/2429/737.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Cora, Vlad M. “Model-based active learning in hierarchical policies .” 2008. Web. 04 Jul 2020.

Vancouver:

Cora VM. Model-based active learning in hierarchical policies . [Internet] [Thesis]. University of British Columbia; 2008. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/2429/737.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cora VM. Model-based active learning in hierarchical policies . [Thesis]. University of British Columbia; 2008. Available from: http://hdl.handle.net/2429/737

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

13. Ganjali, Danyan. Efficient Reinforcement Learning with Bayesian Optimization.

Degree: Mechanical and Aerospace Engineering, 2016, University of California – Irvine

 A probabilistic reinforcement learning algorithm is presented for finding control policies in continuous state and action spaces without a prior knowledge of the dynamics. The… (more)

Subjects/Keywords: Engineering; Bayesian Optimization; Expectation Maximization; Reinforcement Learning

…DISSERTATION Efficient Reinforcement Learning with Bayesian Optimization By Danyan Ganjali Doctor of… …Athanasios Sideris, Chair A probabilistic reinforcement learning algorithm is presented for… …x29; and Reinforcement Learning (RL) are families of algorithms that are used to… …as possible. This is where the Bayesian optimization can be utilized as a direct learning… …Direct Bayesian optimization RL… 

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APA (6th Edition):

Ganjali, D. (2016). Efficient Reinforcement Learning with Bayesian Optimization. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/8d50k642

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ganjali, Danyan. “Efficient Reinforcement Learning with Bayesian Optimization.” 2016. Thesis, University of California – Irvine. Accessed July 04, 2020. http://www.escholarship.org/uc/item/8d50k642.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ganjali, Danyan. “Efficient Reinforcement Learning with Bayesian Optimization.” 2016. Web. 04 Jul 2020.

Vancouver:

Ganjali D. Efficient Reinforcement Learning with Bayesian Optimization. [Internet] [Thesis]. University of California – Irvine; 2016. [cited 2020 Jul 04]. Available from: http://www.escholarship.org/uc/item/8d50k642.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ganjali D. Efficient Reinforcement Learning with Bayesian Optimization. [Thesis]. University of California – Irvine; 2016. Available from: http://www.escholarship.org/uc/item/8d50k642

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Rouault, Marion. Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme.

Degree: Docteur es, Neurosciences, 2015, Paris, Ecole normale supérieure

Le contrôle exécutif de l'action fait référence a la capacité de l'homme a contrôler et adapter son comportement de manière flexible, en lien avec ses… (more)

Subjects/Keywords: Neurosciences cognitives; Cortex préfrontal; Croyances; Apprentissage par renforcement; Inférence bayesienne; Cognitive neuroscience; Prefrontal cortex; Beliefs; Reinforcement learning; Bayesian inference; 616.8

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APA (6th Edition):

Rouault, M. (2015). Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme. (Doctoral Dissertation). Paris, Ecole normale supérieure. Retrieved from http://www.theses.fr/2015ENSU0052

Chicago Manual of Style (16th Edition):

Rouault, Marion. “Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme.” 2015. Doctoral Dissertation, Paris, Ecole normale supérieure. Accessed July 04, 2020. http://www.theses.fr/2015ENSU0052.

MLA Handbook (7th Edition):

Rouault, Marion. “Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme.” 2015. Web. 04 Jul 2020.

Vancouver:

Rouault M. Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme. [Internet] [Doctoral dissertation]. Paris, Ecole normale supérieure; 2015. [cited 2020 Jul 04]. Available from: http://www.theses.fr/2015ENSU0052.

Council of Science Editors:

Rouault M. Integration of beliefs and affective values in human decision-making : Intégration des croyances et valeurs affectives dans la prise de décision chez l'homme. [Doctoral Dissertation]. Paris, Ecole normale supérieure; 2015. Available from: http://www.theses.fr/2015ENSU0052

15. XING ZHE. Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation.

Degree: 2014, National University of Singapore

Subjects/Keywords: music recommendation; collaborative filtering; reinforcement learning; Bayesian graphical model

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APA (6th Edition):

ZHE, X. (2014). Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/118258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

ZHE, XING. “Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation.” 2014. Thesis, National University of Singapore. Accessed July 04, 2020. http://scholarbank.nus.edu.sg/handle/10635/118258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

ZHE, XING. “Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation.” 2014. Web. 04 Jul 2020.

Vancouver:

ZHE X. Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation. [Internet] [Thesis]. National University of Singapore; 2014. [cited 2020 Jul 04]. Available from: http://scholarbank.nus.edu.sg/handle/10635/118258.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

ZHE X. Enhangcing Collaborative Filtering Music recommendation by Balancing Exploration and Exploitation. [Thesis]. National University of Singapore; 2014. Available from: http://scholarbank.nus.edu.sg/handle/10635/118258

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Edinburgh

16. Valton, Vincent. Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia.

Degree: PhD, 2015, University of Edinburgh

 Computational modelling has been gaining an increasing amount of support from the neuroscience community as a tool to assay cognition and computational processes in the… (more)

Subjects/Keywords: 616.89; computational modelling; reinforcement-learning; prediction-error; Bayesian inference; psychotic disorders; psychosis; schizophrenia; delusions; hallucinations; positive-symptoms

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APA (6th Edition):

Valton, V. (2015). Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/21097

Chicago Manual of Style (16th Edition):

Valton, Vincent. “Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia.” 2015. Doctoral Dissertation, University of Edinburgh. Accessed July 04, 2020. http://hdl.handle.net/1842/21097.

MLA Handbook (7th Edition):

Valton, Vincent. “Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia.” 2015. Web. 04 Jul 2020.

Vancouver:

Valton V. Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia. [Internet] [Doctoral dissertation]. University of Edinburgh; 2015. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1842/21097.

Council of Science Editors:

Valton V. Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia. [Doctoral Dissertation]. University of Edinburgh; 2015. Available from: http://hdl.handle.net/1842/21097

17. Asmuth, John Thomas. Model-based Bayesian reinforcement learning with generalized priors.

Degree: Computer Science, 2013, Rutgers University

Subjects/Keywords: Reinforcement learning; Bayesian statistical decision theory

…Restaurant Process . . . . . . . . . . . . . . 26 1.3. Bayesian Models for Reinforcement Learning… …different approaches to Bayesian model-based reinforcement learning that are discussed in this… …Bayesian reinforcement-learning. Exact Bayes-optimal policy inference is generally intractable… …Reinforcement Learning . . . . . . . . . . 31 2. Models and Inference… …67 3.4. Multi-Task Reinforcement Learning . . . . . . . . . . . . . . . . 71 3.5. BEETLE… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Asmuth, J. T. (2013). Model-based Bayesian reinforcement learning with generalized priors. (Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Asmuth, John Thomas. “Model-based Bayesian reinforcement learning with generalized priors.” 2013. Thesis, Rutgers University. Accessed July 04, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Asmuth, John Thomas. “Model-based Bayesian reinforcement learning with generalized priors.” 2013. Web. 04 Jul 2020.

Vancouver:

Asmuth JT. Model-based Bayesian reinforcement learning with generalized priors. [Internet] [Thesis]. Rutgers University; 2013. [cited 2020 Jul 04]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Asmuth JT. Model-based Bayesian reinforcement learning with generalized priors. [Thesis]. Rutgers University; 2013. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade do Rio Grande do Sul

18. Heinen, Milton Roberto. A connectionist approach for incremental function approximation and on-line tasks.

Degree: 2011, Universidade do Rio Grande do Sul

Este trabalho propõe uma nova abordagem conexionista, chamada de IGMN (do inglês Incremental Gaussian Mixture Network), para aproximação incremental de funções e tarefas de tempo… (more)

Subjects/Keywords: Machine learning; Inteligência artificial; Redes bayesianas; Artificial neural networks; Aprendizagem : Maquina; Incremental learning; Cluster; Bayesian methods; Robótica; Gaussian mixture models; Function approximation; Regression; Clustering; Reinforcement learning; Autonomous mobile robots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Heinen, M. R. (2011). A connectionist approach for incremental function approximation and on-line tasks. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/29015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Heinen, Milton Roberto. “A connectionist approach for incremental function approximation and on-line tasks.” 2011. Thesis, Universidade do Rio Grande do Sul. Accessed July 04, 2020. http://hdl.handle.net/10183/29015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Heinen, Milton Roberto. “A connectionist approach for incremental function approximation and on-line tasks.” 2011. Web. 04 Jul 2020.

Vancouver:

Heinen MR. A connectionist approach for incremental function approximation and on-line tasks. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2011. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10183/29015.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Heinen MR. A connectionist approach for incremental function approximation and on-line tasks. [Thesis]. Universidade do Rio Grande do Sul; 2011. Available from: http://hdl.handle.net/10183/29015

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

19. Song, Haobei. Optimal Learning Theory and Approximate Optimal Learning Algorithms.

Degree: 2019, University of Waterloo

 The exploration/exploitation dilemma is a fundamental but often computationally intractable problem in reinforcement learning. The dilemma also impacts data efficiency which can be pivotal when… (more)

Subjects/Keywords: reinforcement learning; machine learning; exploration; exploitation; optimal learning; Bayesian reinforcement learning; model based reinforcement learning; neural network

…control literature. The initial work on model based Bayesian Reinforcement Learning was… …based reinforcement learning In Bayesian statistics, the belief about the parameters p1 , p2… …computation efficiency in a Bayesian model based reinforcement learning framework, since more… …collides with the Bayesian model based reinforcement learning in many aspects as will be shown in… …optimal learning algorithm, in this case to approximate Bayesian reinforcement learning… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Song, H. (2019). Optimal Learning Theory and Approximate Optimal Learning Algorithms. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15042

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Song, Haobei. “Optimal Learning Theory and Approximate Optimal Learning Algorithms.” 2019. Thesis, University of Waterloo. Accessed July 04, 2020. http://hdl.handle.net/10012/15042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Song, Haobei. “Optimal Learning Theory and Approximate Optimal Learning Algorithms.” 2019. Web. 04 Jul 2020.

Vancouver:

Song H. Optimal Learning Theory and Approximate Optimal Learning Algorithms. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10012/15042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Song H. Optimal Learning Theory and Approximate Optimal Learning Algorithms. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/15042

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

20. Skordilis, Erotokritos. Bayesian Filtering Methods For Dynamic System Monitoring and Control.

Degree: PhD, Industrial Engineering (Engineering), 2019, University of Miami

  Real-time system monitoring and control represent two of the most important issues that characterize modern industries in critical areas of civilian and military interest,… (more)

Subjects/Keywords: Bayesian filtering; Reinforcement learning; dynamic control; System maintenance

…113 6 DEEP REINFORCEMENT LEARNING FOR REAL-TIME DECISIONMAKING 6.1 118 Bayesian Filtering… …Applications Bayesian filters and reinforcement learning have been used in many studies with… …Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . 51 4 MONITORING AND CONTROL… …Decision Making With Reinforcement Learning . . 132 6.2.1.1 Results for the Simulated Dataset… …neural network. . . . . . . . . . . . . . . . . . . 50 7 Reinforcement learning process… 

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APA (6th Edition):

Skordilis, E. (2019). Bayesian Filtering Methods For Dynamic System Monitoring and Control. (Doctoral Dissertation). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_dissertations/2404

Chicago Manual of Style (16th Edition):

Skordilis, Erotokritos. “Bayesian Filtering Methods For Dynamic System Monitoring and Control.” 2019. Doctoral Dissertation, University of Miami. Accessed July 04, 2020. https://scholarlyrepository.miami.edu/oa_dissertations/2404.

MLA Handbook (7th Edition):

Skordilis, Erotokritos. “Bayesian Filtering Methods For Dynamic System Monitoring and Control.” 2019. Web. 04 Jul 2020.

Vancouver:

Skordilis E. Bayesian Filtering Methods For Dynamic System Monitoring and Control. [Internet] [Doctoral dissertation]. University of Miami; 2019. [cited 2020 Jul 04]. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/2404.

Council of Science Editors:

Skordilis E. Bayesian Filtering Methods For Dynamic System Monitoring and Control. [Doctoral Dissertation]. University of Miami; 2019. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/2404

21. Findling, Charles. Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine.

Degree: Docteur es, Sciences cognitives, 2018, Sorbonne université

Dans des environnements incertains et changeants, prendre des décisions nécessite l’analyse et la pondération des informations passées et présentes. Pour modéliser le comportement humain dans… (more)

Subjects/Keywords: Bruit computationnel; Inférence Bayesienne; Apprentissage par renforcement; Prise de décision; Processus cognitif; Neurosciences; Computational noise; Bayesian interference; Reinforcement learning; Decision Making; Cognitive process; Neurosciences; 006.3

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Findling, C. (2018). Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine. (Doctoral Dissertation). Sorbonne université. Retrieved from http://www.theses.fr/2018SORUS490

Chicago Manual of Style (16th Edition):

Findling, Charles. “Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine.” 2018. Doctoral Dissertation, Sorbonne université. Accessed July 04, 2020. http://www.theses.fr/2018SORUS490.

MLA Handbook (7th Edition):

Findling, Charles. “Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine.” 2018. Web. 04 Jul 2020.

Vancouver:

Findling C. Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine. [Internet] [Doctoral dissertation]. Sorbonne université; 2018. [cited 2020 Jul 04]. Available from: http://www.theses.fr/2018SORUS490.

Council of Science Editors:

Findling C. Computational learning noise in human decision-making : Bruit d'apprentissage dans la prise de décision humaine. [Doctoral Dissertation]. Sorbonne université; 2018. Available from: http://www.theses.fr/2018SORUS490

22. Colombo, Matteo. Complying with norms : a neurocomputational exploration.

Degree: PhD, 2012, University of Edinburgh

 The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in… (more)

Subjects/Keywords: 306; social norms; reinforcement learning; Bayesian modelling; neural computing

…consists of Bayesian Reinforcement Learning algorithms implemented by activity in certain neural… …beginnings of a model of norm compliance behaviour grounded on Bayesian - Reinforcement Learning… …Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What… …Bayes in the Brain. On Bayesian Modelling in Neuroscience.” The British Journal for Philosophy… …psychological mechanism of moral judgement can be described within the RL - Bayesian… 

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APA (6th Edition):

Colombo, M. (2012). Complying with norms : a neurocomputational exploration. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/6462

Chicago Manual of Style (16th Edition):

Colombo, Matteo. “Complying with norms : a neurocomputational exploration.” 2012. Doctoral Dissertation, University of Edinburgh. Accessed July 04, 2020. http://hdl.handle.net/1842/6462.

MLA Handbook (7th Edition):

Colombo, Matteo. “Complying with norms : a neurocomputational exploration.” 2012. Web. 04 Jul 2020.

Vancouver:

Colombo M. Complying with norms : a neurocomputational exploration. [Internet] [Doctoral dissertation]. University of Edinburgh; 2012. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1842/6462.

Council of Science Editors:

Colombo M. Complying with norms : a neurocomputational exploration. [Doctoral Dissertation]. University of Edinburgh; 2012. Available from: http://hdl.handle.net/1842/6462


Université de Lorraine

23. Araya-López, Mauricio. Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems.

Degree: Docteur es, Informatique, 2013, Université de Lorraine

Cette thèse s'intéresse à des problèmes de prise de décision séquentielle dans lesquels l'acquisition d'information est une fin en soi. Plus précisément, elle cherche d'abord… (more)

Subjects/Keywords: Collecte d'informations; Transitions optimistes; POMDP; Apprentissage par renforcement bayésien; Apprentissage du modèle d'un MDP; Problèmes de prise de décision séquentielle; Modèles bayésiens; Information-Gathering; Optimistic Transitions; POMDP; Bayesian Reinforcement Learning; MDP Model Learning; Sequential Decision Problems; Bayesian Models; 006.33

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Araya-López, M. (2013). Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems. (Doctoral Dissertation). Université de Lorraine. Retrieved from http://www.theses.fr/2013LORR0002

Chicago Manual of Style (16th Edition):

Araya-López, Mauricio. “Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems.” 2013. Doctoral Dissertation, Université de Lorraine. Accessed July 04, 2020. http://www.theses.fr/2013LORR0002.

MLA Handbook (7th Edition):

Araya-López, Mauricio. “Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems.” 2013. Web. 04 Jul 2020.

Vancouver:

Araya-López M. Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems. [Internet] [Doctoral dissertation]. Université de Lorraine; 2013. [cited 2020 Jul 04]. Available from: http://www.theses.fr/2013LORR0002.

Council of Science Editors:

Araya-López M. Des algorithmes presque optimaux pour les problèmes de décision séquentielle à des fins de collecte d'information : Near-Optimal Algorithms for Sequential Information-Gathering Decision Problems. [Doctoral Dissertation]. Université de Lorraine; 2013. Available from: http://www.theses.fr/2013LORR0002


University of Oxford

24. McInerney, Robert E. Decision making under uncertainty.

Degree: PhD, 2014, University of Oxford

 Operating and interacting in an environment requires the ability to manage uncertainty and to choose definite courses of action. In this thesis we look to… (more)

Subjects/Keywords: 006.3; Probability theory and stochastic processes; Artificial Intelligence; Probability; Stochastic processes; Computing; Applications and algorithms; Information engineering; Robotics; Engineering & allied sciences; machine learning; probability theory; Bayesian; decision making; Reinforcement Learning; Gaussian Process; inference; approximate inference; Multi-armed Bandit; optimal decision making; uncertainty; managing uncertainty

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APA (6th Edition):

McInerney, R. E. (2014). Decision making under uncertainty. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860

Chicago Manual of Style (16th Edition):

McInerney, Robert E. “Decision making under uncertainty.” 2014. Doctoral Dissertation, University of Oxford. Accessed July 04, 2020. http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860.

MLA Handbook (7th Edition):

McInerney, Robert E. “Decision making under uncertainty.” 2014. Web. 04 Jul 2020.

Vancouver:

McInerney RE. Decision making under uncertainty. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2020 Jul 04]. Available from: http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860.

Council of Science Editors:

McInerney RE. Decision making under uncertainty. [Doctoral Dissertation]. University of Oxford; 2014. Available from: http://ora.ox.ac.uk/objects/uuid:a34e87ad-8330-42df-8ba6-d55f10529331 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692860

25. Leike, Jan. Nonparametric General Reinforcement Learning .

Degree: 2016, Australian National University

Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs). In this thesis we go beyond MDPs and consider reinforcement learning in… (more)

Subjects/Keywords: Bayesian methods; sequence prediction; merging; general reinforcement learning; universal artificial intelligence; AIXI; Thompson sampling; knowledge-seeking agents; Pareto optimality; intelligence; asymptotic optimality; computability; reflective oracle; grain of truth problem; Nash equilibrium

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APA (6th Edition):

Leike, J. (2016). Nonparametric General Reinforcement Learning . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/111080

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Leike, Jan. “Nonparametric General Reinforcement Learning .” 2016. Thesis, Australian National University. Accessed July 04, 2020. http://hdl.handle.net/1885/111080.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Leike, Jan. “Nonparametric General Reinforcement Learning .” 2016. Web. 04 Jul 2020.

Vancouver:

Leike J. Nonparametric General Reinforcement Learning . [Internet] [Thesis]. Australian National University; 2016. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1885/111080.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Leike J. Nonparametric General Reinforcement Learning . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/111080

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

26. Théro, Héloïse. Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making.

Degree: Docteur es, Neurosciences cognitives, 2018, Paris Sciences et Lettres

Le sentiment d’agentivité est défini comme le sentiment de contrôler nos actions, et à travers elles, les évènements du monde extérieur. Cet ensemble phénoménologique dépend… (more)

Subjects/Keywords: Agentivité; Contrôle instrumental; Inférence causale; Prise de décision basée sur des valeurs; Modèles d’apprentissage par renforcement; Modèles bayésien; Agency; Instrumental control; Causal inference; Value-based decision-making; Reinforcement learning models; Bayesian models; 616.8; 153

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APA (6th Edition):

Théro, H. (2018). Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making. (Doctoral Dissertation). Paris Sciences et Lettres. Retrieved from http://www.theses.fr/2018PSLEE028

Chicago Manual of Style (16th Edition):

Théro, Héloïse. “Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making.” 2018. Doctoral Dissertation, Paris Sciences et Lettres. Accessed July 04, 2020. http://www.theses.fr/2018PSLEE028.

MLA Handbook (7th Edition):

Théro, Héloïse. “Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making.” 2018. Web. 04 Jul 2020.

Vancouver:

Théro H. Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making. [Internet] [Doctoral dissertation]. Paris Sciences et Lettres; 2018. [cited 2020 Jul 04]. Available from: http://www.theses.fr/2018PSLEE028.

Council of Science Editors:

Théro H. Contrôle, agentivité et apprentissage par renforcement : Control, agency and reinforcement learning in human decision-making. [Doctoral Dissertation]. Paris Sciences et Lettres; 2018. Available from: http://www.theses.fr/2018PSLEE028

27. Van Witteveen, K. (author). Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control.

Degree: 2014, Delft University of Technology

This thesis investigates the applicability of the Probabilistic Inference for Learning COntrol (PILCO) algorithm to large systems and systems with time varying measurement noise. PILCO… (more)

Subjects/Keywords: reinforcement learning; Bayesian inference; Gaussian Process; adaptive nonlinear control; parallel computing

…5-2 Part 2: Model-based Reinforcement Learning 41 42… …Chapter 1 Introduction Reinforcement Learning (RL) is a control method which learns… …65 8-1-1 Sub-objective 1: Identify if the GP controller has learning advantages over the… …43 5-3 Computational time reduction: realized time increase for parallel policy learning… …measurement noise variances 64 7-7 A-PILCO: convergence comparison of policy learning with and… 

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APA (6th Edition):

Van Witteveen, K. (. (2014). Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e510b316-668d-48aa-aa01-c9a5f97073cb

Chicago Manual of Style (16th Edition):

Van Witteveen, K (author). “Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control.” 2014. Masters Thesis, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:e510b316-668d-48aa-aa01-c9a5f97073cb.

MLA Handbook (7th Edition):

Van Witteveen, K (author). “Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control.” 2014. Web. 04 Jul 2020.

Vancouver:

Van Witteveen K(. Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control. [Internet] [Masters thesis]. Delft University of Technology; 2014. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:e510b316-668d-48aa-aa01-c9a5f97073cb.

Council of Science Editors:

Van Witteveen K(. Adaptive Reinforcement Learning: Increasing the applicability for large and time varying systems using parallel Gaussian Process regression and adaptive nonlinear control. [Masters Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:e510b316-668d-48aa-aa01-c9a5f97073cb

28. Rodenburg, Kathleen. Choice Under Uncertainty: Violations of Optimality in Decision Making.

Degree: 2013, University of Guelph

 This thesis is an investigation of how subjects behave in an individual binary choice decision task with the option to purchase or observe for free… (more)

Subjects/Keywords: Economics Rational Decision Theory Experimental and Behavioural Economics Bayesian Expected Utility Theory Reinforcement Learning Models status quo bias uncertainty imperfect information risk preferences

…conducted in economics that tests Reinforcement Learning relative to Bayesian belief based… …reinforcement learning relative to Bayesian learning in the context of simple non-strategic decision… …benchmark Reinforcement Learning varies by discipline (economics and psychology literature… …designed to test Reinforcement learning in economics were conducted in the context of a strategic… …findings from these studies are twofold: 1) The Reinforcement Learning model outperformed… 

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APA (6th Edition):

Rodenburg, K. (2013). Choice Under Uncertainty: Violations of Optimality in Decision Making. (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7245

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rodenburg, Kathleen. “Choice Under Uncertainty: Violations of Optimality in Decision Making.” 2013. Thesis, University of Guelph. Accessed July 04, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7245.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rodenburg, Kathleen. “Choice Under Uncertainty: Violations of Optimality in Decision Making.” 2013. Web. 04 Jul 2020.

Vancouver:

Rodenburg K. Choice Under Uncertainty: Violations of Optimality in Decision Making. [Internet] [Thesis]. University of Guelph; 2013. [cited 2020 Jul 04]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7245.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rodenburg K. Choice Under Uncertainty: Violations of Optimality in Decision Making. [Thesis]. University of Guelph; 2013. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7245

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

29. Huang, Yanping. Bayesian Computation and Optimal Decision Making in Primate Brains.

Degree: PhD, 2015, University of Washington

 This dissertation investigates the computational principles underlying the brains’ remarkable capacity to perceive, learn and act in environments of constantly varying uncertainty. Bayesian probability theory… (more)

Subjects/Keywords: Bayesian Computation; Decision making; Markov Decision Process; Neural Networks; Reinforcement learning; Sequential Model; Artificial intelligence; Cognitive psychology; Neurosciences; computer science and engineering

reinforcement learning (RL) task can be quite difficult and time-consuming. Recent… …Chapter 2 NEURONS AS MONTE CARLO SAMPLERS: BAYESIAN INFERENCE AND LEARNING IN SPIKING NETWORKS… …Bayesian inference and learning in a great variety of tasks in perception, sensori-motor… …2.8 Sensory Adaptation and Bayesian Filtering. (a) The hidden state (… …the last lap. The center of the place field shifted backwards after learning… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, Y. (2015). Bayesian Computation and Optimal Decision Making in Primate Brains. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/33689

Chicago Manual of Style (16th Edition):

Huang, Yanping. “Bayesian Computation and Optimal Decision Making in Primate Brains.” 2015. Doctoral Dissertation, University of Washington. Accessed July 04, 2020. http://hdl.handle.net/1773/33689.

MLA Handbook (7th Edition):

Huang, Yanping. “Bayesian Computation and Optimal Decision Making in Primate Brains.” 2015. Web. 04 Jul 2020.

Vancouver:

Huang Y. Bayesian Computation and Optimal Decision Making in Primate Brains. [Internet] [Doctoral dissertation]. University of Washington; 2015. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1773/33689.

Council of Science Editors:

Huang Y. Bayesian Computation and Optimal Decision Making in Primate Brains. [Doctoral Dissertation]. University of Washington; 2015. Available from: http://hdl.handle.net/1773/33689

30. Lee, Steven Fong-Yi. Probabilistic semantics for vagueness.

Degree: PhD, Philosophy, 2019, University of Illinois – Urbana-Champaign

 In this dissertation I argue that truth-conditional semantics for vague predicates, combined with a Bayesian account of statistical inference incorporating knowledge of truth-conditions of utterances,… (more)

Subjects/Keywords: semantics; pragmatics; vagueness; probability; game theory; artificial intelligence; reinforcement learning; Bayes; Bayesian; linguistics; philosophy; statistical inference; cognitive science; Grice; formal semantics; Montague; Sorites; logic; dynamic semantics; truth-conditions

…work from O’Connor (2014), which draws on reinforcement learning to explain how… …to hearing that sentence. Their model is a development of Gricean pragmatics along Bayesian… …plausible Bayesian model of how, given utterances containing vague predicates, listeners draw the… …a non-Bayesian model for how we can go from knowledge of the truth-conditions of ‘Feynman… …of which were discussed in Chapter 3. Bayesian calculations based on anything recognizable… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lee, S. F. (2019). Probabilistic semantics for vagueness. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/106242

Chicago Manual of Style (16th Edition):

Lee, Steven Fong-Yi. “Probabilistic semantics for vagueness.” 2019. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 04, 2020. http://hdl.handle.net/2142/106242.

MLA Handbook (7th Edition):

Lee, Steven Fong-Yi. “Probabilistic semantics for vagueness.” 2019. Web. 04 Jul 2020.

Vancouver:

Lee SF. Probabilistic semantics for vagueness. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/2142/106242.

Council of Science Editors:

Lee SF. Probabilistic semantics for vagueness. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/106242

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