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You searched for subject:(Machine reinforcement learning). Showing records 1 – 30 of 200 total matches.

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Oregon State University

1. Wilson, Aaron (Aaron Creighton). Bayesian methods for knowledge transfer and policy search in reinforcement learning.

Degree: PhD, Computer Science, 2012, Oregon State University

 How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action… (more)

Subjects/Keywords: Machine Learning; Reinforcement learning

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APA (6th Edition):

Wilson, A. (. C. (2012). Bayesian methods for knowledge transfer and policy search in reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34550

Chicago Manual of Style (16th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Doctoral Dissertation, Oregon State University. Accessed October 19, 2019. http://hdl.handle.net/1957/34550.

MLA Handbook (7th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Web. 19 Oct 2019.

Vancouver:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1957/34550.

Council of Science Editors:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/34550


Rutgers University

2. Marivate, Vukosi N. Improved empirical methods in reinforcement-learning evaluation.

Degree: PhD, Computer Science, 2015, Rutgers University

The central question addressed in this research is ”can we define evaluation methodologies that encourage reinforcement-learning (RL) algorithms to work effectively with real-life data?” First,… (more)

Subjects/Keywords: Reinforcement learning; Machine learning; Algorithms

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APA (6th Edition):

Marivate, V. N. (2015). Improved empirical methods in reinforcement-learning evaluation. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46389/

Chicago Manual of Style (16th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Doctoral Dissertation, Rutgers University. Accessed October 19, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

MLA Handbook (7th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Web. 19 Oct 2019.

Vancouver:

Marivate VN. Improved empirical methods in reinforcement-learning evaluation. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2019 Oct 19]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

Council of Science Editors:

Marivate VN. Improved empirical methods in reinforcement-learning evaluation. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/


Hong Kong University of Science and Technology

3. Mo, Kaixiang CSE. Transfer reinforcement learning for task-oriented dialogue systems.

Degree: 2018, Hong Kong University of Science and Technology

 Dialogue systems are attracting more and more attention recently. Dialogue systems can be categorized into open-domain dialogue systems and task-oriented dialogue systems. Task-oriented dialogue systems… (more)

Subjects/Keywords: Reinforcement learning; Machine learning; Dialogue analysis

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APA (6th Edition):

Mo, K. C. (2018). Transfer reinforcement learning for task-oriented dialogue systems. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012596567703412 ; http://repository.ust.hk/ir/bitstream/1783.1-92234/1/th_redirect.html

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):

Mo, Kaixiang CSE. “Transfer reinforcement learning for task-oriented dialogue systems.” 2018. Thesis, Hong Kong University of Science and Technology. Accessed October 19, 2019. https://doi.org/10.14711/thesis-991012596567703412 ; http://repository.ust.hk/ir/bitstream/1783.1-92234/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Mo, Kaixiang CSE. “Transfer reinforcement learning for task-oriented dialogue systems.” 2018. Web. 19 Oct 2019.

Vancouver:

Mo KC. Transfer reinforcement learning for task-oriented dialogue systems. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2018. [cited 2019 Oct 19]. Available from: https://doi.org/10.14711/thesis-991012596567703412 ; http://repository.ust.hk/ir/bitstream/1783.1-92234/1/th_redirect.html.

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

Council of Science Editors:

Mo KC. Transfer reinforcement learning for task-oriented dialogue systems. [Thesis]. Hong Kong University of Science and Technology; 2018. Available from: https://doi.org/10.14711/thesis-991012596567703412 ; http://repository.ust.hk/ir/bitstream/1783.1-92234/1/th_redirect.html

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


University of New South Wales

4. Harris, Sean. Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND.

Degree: Computer Science & Engineering, 2017, University of New South Wales

 As robots become increasingly common in modern society, the need for effective machine learning of robot tasks is becoming more significant. Hierarchical Reinforcement Learning (HRL)… (more)

Subjects/Keywords: Machine Learning; Hierarchical Reinforcement Learning; Robotics

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APA (6th Edition):

Harris, S. (2017). Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60244 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51036/SOURCE2?view=true

Chicago Manual of Style (16th Edition):

Harris, Sean. “Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND.” 2017. Doctoral Dissertation, University of New South Wales. Accessed October 19, 2019. http://handle.unsw.edu.au/1959.4/60244 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51036/SOURCE2?view=true.

MLA Handbook (7th Edition):

Harris, Sean. “Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND.” 2017. Web. 19 Oct 2019.

Vancouver:

Harris S. Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Oct 19]. Available from: http://handle.unsw.edu.au/1959.4/60244 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51036/SOURCE2?view=true.

Council of Science Editors:

Harris S. Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/60244 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51036/SOURCE2?view=true

5. Mukherjee, Subhasis. Applying reinforcement learning in playing Robosoccer using the AIBO.

Degree: Master of Computing (by research), 2010, Federation University Australia

"Robosoccer is a popular test bed for AI programs around the world in which AIBO entertainments robots take part in the middle sized soccer event.… (more)

Subjects/Keywords: Robotics; Artificial intelligence; Reinforcement learning; Machine learning

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APA (6th Edition):

Mukherjee, S. (2010). Applying reinforcement learning in playing Robosoccer using the AIBO. (Masters Thesis). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39872

Chicago Manual of Style (16th Edition):

Mukherjee, Subhasis. “Applying reinforcement learning in playing Robosoccer using the AIBO.” 2010. Masters Thesis, Federation University Australia. Accessed October 19, 2019. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39872.

MLA Handbook (7th Edition):

Mukherjee, Subhasis. “Applying reinforcement learning in playing Robosoccer using the AIBO.” 2010. Web. 19 Oct 2019.

Vancouver:

Mukherjee S. Applying reinforcement learning in playing Robosoccer using the AIBO. [Internet] [Masters thesis]. Federation University Australia; 2010. [cited 2019 Oct 19]. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39872.

Council of Science Editors:

Mukherjee S. Applying reinforcement learning in playing Robosoccer using the AIBO. [Masters Thesis]. Federation University Australia; 2010. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39872


University of Waterloo

6. Tse, Timmy Rong Tian. Model-Based Bayesian Sparse Sampling for Data Efficient Control.

Degree: 2019, University of Waterloo

 In this work, we propose a novel Bayesian-inspired model-based policy search algorithm for data efficient control. In contrast to other model-based approaches, our algorithm makes… (more)

Subjects/Keywords: machine learning; reinforcement learning; artificial intelligence

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APA (6th Edition):

Tse, T. R. T. (2019). Model-Based Bayesian Sparse Sampling for Data Efficient Control. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14774

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):

Tse, Timmy Rong Tian. “Model-Based Bayesian Sparse Sampling for Data Efficient Control.” 2019. Thesis, University of Waterloo. Accessed October 19, 2019. http://hdl.handle.net/10012/14774.

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

MLA Handbook (7th Edition):

Tse, Timmy Rong Tian. “Model-Based Bayesian Sparse Sampling for Data Efficient Control.” 2019. Web. 19 Oct 2019.

Vancouver:

Tse TRT. Model-Based Bayesian Sparse Sampling for Data Efficient Control. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10012/14774.

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

Council of Science Editors:

Tse TRT. Model-Based Bayesian Sparse Sampling for Data Efficient Control. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14774

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


Cal Poly

7. Weideman, Ryan. Robot Navigation in Cluttered Environments with Deep Reinforcement Learning.

Degree: MS, Electrical Engineering, 2019, Cal Poly

  The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that… (more)

Subjects/Keywords: robotics; reinforcement learning; navigation; machine learning; Robotics

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APA (6th Edition):

Weideman, R. (2019). Robot Navigation in Cluttered Environments with Deep Reinforcement Learning. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/2011

Chicago Manual of Style (16th Edition):

Weideman, Ryan. “Robot Navigation in Cluttered Environments with Deep Reinforcement Learning.” 2019. Masters Thesis, Cal Poly. Accessed October 19, 2019. https://digitalcommons.calpoly.edu/theses/2011.

MLA Handbook (7th Edition):

Weideman, Ryan. “Robot Navigation in Cluttered Environments with Deep Reinforcement Learning.” 2019. Web. 19 Oct 2019.

Vancouver:

Weideman R. Robot Navigation in Cluttered Environments with Deep Reinforcement Learning. [Internet] [Masters thesis]. Cal Poly; 2019. [cited 2019 Oct 19]. Available from: https://digitalcommons.calpoly.edu/theses/2011.

Council of Science Editors:

Weideman R. Robot Navigation in Cluttered Environments with Deep Reinforcement Learning. [Masters Thesis]. Cal Poly; 2019. Available from: https://digitalcommons.calpoly.edu/theses/2011


Victoria University of Wellington

8. Peng, Yiming. Policy Direct Search for Effective Reinforcement Learning.

Degree: 2019, Victoria University of Wellington

Reinforcement Learning (RL) problems appear in diverse real-world applications and are gaining substantial attention in academia and industry. Policy Direct Search (PDS) is widely recognized… (more)

Subjects/Keywords: Reinforcement Learning; Machine learning; Artificial intelligence

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APA (6th Edition):

Peng, Y. (2019). Policy Direct Search for Effective Reinforcement Learning. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/8289

Chicago Manual of Style (16th Edition):

Peng, Yiming. “Policy Direct Search for Effective Reinforcement Learning.” 2019. Doctoral Dissertation, Victoria University of Wellington. Accessed October 19, 2019. http://hdl.handle.net/10063/8289.

MLA Handbook (7th Edition):

Peng, Yiming. “Policy Direct Search for Effective Reinforcement Learning.” 2019. Web. 19 Oct 2019.

Vancouver:

Peng Y. Policy Direct Search for Effective Reinforcement Learning. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10063/8289.

Council of Science Editors:

Peng Y. Policy Direct Search for Effective Reinforcement Learning. [Doctoral Dissertation]. Victoria University of Wellington; 2019. Available from: http://hdl.handle.net/10063/8289


University of Illinois – Chicago

9. Tirinzoni, Andrea. Adversarial Inverse Reinforcement Learning with Changing Dynamics.

Degree: 2017, University of Illinois – Chicago

 Most work on inverse reinforcement learning, the problem of recovering the unknown reward function being optimized by a decision-making agent, has focused on cases where… (more)

Subjects/Keywords: Machine Learning; Inverse Reinforcement Learning; Reinforcement Learning; Adversarial Prediction; Markov Decision Process; Imitation Learning

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APA (6th Edition):

Tirinzoni, A. (2017). Adversarial Inverse Reinforcement Learning with Changing Dynamics. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22081

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):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Thesis, University of Illinois – Chicago. Accessed October 19, 2019. http://hdl.handle.net/10027/22081.

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

MLA Handbook (7th Edition):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Web. 19 Oct 2019.

Vancouver:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10027/22081.

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

Council of Science Editors:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22081

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


Victoria University of Wellington

10. Bebbington, James. Learning Actions That Reduce Variation in Objects.

Degree: 2011, Victoria University of Wellington

 The variation in the data that a robot in the real world receives from its sensory inputs (i.e. its sensory data) will come from many… (more)

Subjects/Keywords: Reinforcement learning; Restricted Boltzmann Machine; Recognition; Machine learning

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APA (6th Edition):

Bebbington, J. (2011). Learning Actions That Reduce Variation in Objects. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2295

Chicago Manual of Style (16th Edition):

Bebbington, James. “Learning Actions That Reduce Variation in Objects.” 2011. Masters Thesis, Victoria University of Wellington. Accessed October 19, 2019. http://hdl.handle.net/10063/2295.

MLA Handbook (7th Edition):

Bebbington, James. “Learning Actions That Reduce Variation in Objects.” 2011. Web. 19 Oct 2019.

Vancouver:

Bebbington J. Learning Actions That Reduce Variation in Objects. [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10063/2295.

Council of Science Editors:

Bebbington J. Learning Actions That Reduce Variation in Objects. [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/2295


University of Waterloo

11. 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

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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 October 19, 2019. 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. 19 Oct 2019.

Vancouver:

Song H. Optimal Learning Theory and Approximate Optimal Learning Algorithms. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Oct 19]. 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


University of Southern California

12. Kalakrishnan, Mrinal. Learning objective functions for autonomous motion generation.

Degree: PhD, Computer Science, 2014, University of Southern California

 Planning and optimization methods have been widely applied to the problem of trajectory generation for autonomous robotics. The performance of such methods, however, is critically… (more)

Subjects/Keywords: robotics; machine learning; motion planning; trajectory optimization; inverse reinforcement learning; reinforcement learning; locomotion; manipulation

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APA (6th Edition):

Kalakrishnan, M. (2014). Learning objective functions for autonomous motion generation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3781

Chicago Manual of Style (16th Edition):

Kalakrishnan, Mrinal. “Learning objective functions for autonomous motion generation.” 2014. Doctoral Dissertation, University of Southern California. Accessed October 19, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3781.

MLA Handbook (7th Edition):

Kalakrishnan, Mrinal. “Learning objective functions for autonomous motion generation.” 2014. Web. 19 Oct 2019.

Vancouver:

Kalakrishnan M. Learning objective functions for autonomous motion generation. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Oct 19]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3781.

Council of Science Editors:

Kalakrishnan M. Learning objective functions for autonomous motion generation. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3781


Kansas State University

13. Behzadan, Vahid. Security of deep reinforcement learning.

Degree: PhD, Department of Computer Science, 2019, Kansas State University

 Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest from both the research and the industrial communities in the… (more)

Subjects/Keywords: Reinforcement learning; Machine learning; Adversarial machine learning; Policy learning; Security; Artificial Intelligence

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

APA (6th Edition):

Behzadan, V. (2019). Security of deep reinforcement learning. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/39799

Chicago Manual of Style (16th Edition):

Behzadan, Vahid. “Security of deep reinforcement learning.” 2019. Doctoral Dissertation, Kansas State University. Accessed October 19, 2019. http://hdl.handle.net/2097/39799.

MLA Handbook (7th Edition):

Behzadan, Vahid. “Security of deep reinforcement learning.” 2019. Web. 19 Oct 2019.

Vancouver:

Behzadan V. Security of deep reinforcement learning. [Internet] [Doctoral dissertation]. Kansas State University; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2097/39799.

Council of Science Editors:

Behzadan V. Security of deep reinforcement learning. [Doctoral Dissertation]. Kansas State University; 2019. Available from: http://hdl.handle.net/2097/39799


Northeastern University

14. Doty, Kevin. Representation Learning For Control.

Degree: MS, Department of Electrical and Computer Engineering, 2019, Northeastern University

 State representation learning finds an embedding from a high dimensional observation space to a lower dimensional and information dense state space, without supervision. Effective state… (more)

Subjects/Keywords: Machine Learning; Reinforcement Learning; Representation Learning; State Abstraction; Computer engineering

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APA (6th Edition):

Doty, K. (2019). Representation Learning For Control. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20317893

Chicago Manual of Style (16th Edition):

Doty, Kevin. “Representation Learning For Control.” 2019. Masters Thesis, Northeastern University. Accessed October 19, 2019. http://hdl.handle.net/2047/D20317893.

MLA Handbook (7th Edition):

Doty, Kevin. “Representation Learning For Control.” 2019. Web. 19 Oct 2019.

Vancouver:

Doty K. Representation Learning For Control. [Internet] [Masters thesis]. Northeastern University; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/2047/D20317893.

Council of Science Editors:

Doty K. Representation Learning For Control. [Masters Thesis]. Northeastern University; 2019. Available from: http://hdl.handle.net/2047/D20317893


Colorado State University

15. Elliott, Daniel L. Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The.

Degree: PhD, Computer Science, 2018, Colorado State University

Reinforcement learning agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the… (more)

Subjects/Keywords: machine learning; Q-learning; ensemble; reinforcement learning; neural networks

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APA (6th Edition):

Elliott, D. L. (2018). Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/191477

Chicago Manual of Style (16th Edition):

Elliott, Daniel L. “Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Doctoral Dissertation, Colorado State University. Accessed October 19, 2019. http://hdl.handle.net/10217/191477.

MLA Handbook (7th Edition):

Elliott, Daniel L. “Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Web. 19 Oct 2019.

Vancouver:

Elliott DL. Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10217/191477.

Council of Science Editors:

Elliott DL. Wisdom of the crowd : reliable deep reinforcement learning through ensembles of Q-functions, The. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191477


University of Alberta

16. Ávila Pires, Bernardo. Statistical analysis of L1-penalized linear estimation with applications.

Degree: MS, Department of Computing Science, 2011, University of Alberta

 We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case… (more)

Subjects/Keywords: linear estimation; linear regression; machine learning; Lasso; excess risk; reinforcement learning

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APA (6th Edition):

Ávila Pires, B. (2011). Statistical analysis of L1-penalized linear estimation with applications. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/dr26xz283

Chicago Manual of Style (16th Edition):

Ávila Pires, Bernardo. “Statistical analysis of L1-penalized linear estimation with applications.” 2011. Masters Thesis, University of Alberta. Accessed October 19, 2019. https://era.library.ualberta.ca/files/dr26xz283.

MLA Handbook (7th Edition):

Ávila Pires, Bernardo. “Statistical analysis of L1-penalized linear estimation with applications.” 2011. Web. 19 Oct 2019.

Vancouver:

Ávila Pires B. Statistical analysis of L1-penalized linear estimation with applications. [Internet] [Masters thesis]. University of Alberta; 2011. [cited 2019 Oct 19]. Available from: https://era.library.ualberta.ca/files/dr26xz283.

Council of Science Editors:

Ávila Pires B. Statistical analysis of L1-penalized linear estimation with applications. [Masters Thesis]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/dr26xz283


Oregon State University

17. 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 October 19, 2019. http://hdl.handle.net/1957/9096.

MLA Handbook (7th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Web. 19 Oct 2019.

Vancouver:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2019 Oct 19]. 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


Universiteit Utrecht

18. Denissen, N.P.M. Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning.

Degree: 2015, Universiteit Utrecht

 Data rich applications often have to load large amounts of data upon launch. The launch times for these applications, e.g. Facebook and NU.nl, can be… (more)

Subjects/Keywords: Intelligent; Agents; Machine; Learning; Mobile; Application; Prediction; Q-learning; reinforcement; MAS

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APA (6th Edition):

Denissen, N. P. M. (2015). Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/318233

Chicago Manual of Style (16th Edition):

Denissen, N P M. “Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning.” 2015. Masters Thesis, Universiteit Utrecht. Accessed October 19, 2019. http://dspace.library.uu.nl:8080/handle/1874/318233.

MLA Handbook (7th Edition):

Denissen, N P M. “Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning.” 2015. Web. 19 Oct 2019.

Vancouver:

Denissen NPM. Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2019 Oct 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/318233.

Council of Science Editors:

Denissen NPM. Predicting App Launches on Mobile Devices Using Intelligent Agents and Machine Learning. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/318233


Hong Kong University of Science and Technology

19. Choi, Ping-Man. Reinforcement learning in nonstationary environments.

Degree: 2000, Hong Kong University of Science and Technology

Learning to act optimally in the complex world has long been a major goal in artificial intelligence research. Reinforcement learning (RL) is an active research… (more)

Subjects/Keywords: Reinforcement learning (Machine learning)

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

APA (6th Edition):

Choi, P. (2000). Reinforcement learning in nonstationary environments. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b655883 ; http://repository.ust.hk/ir/bitstream/1783.1-1522/1/th_redirect.html

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):

Choi, Ping-Man. “Reinforcement learning in nonstationary environments.” 2000. Thesis, Hong Kong University of Science and Technology. Accessed October 19, 2019. https://doi.org/10.14711/thesis-b655883 ; http://repository.ust.hk/ir/bitstream/1783.1-1522/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Choi, Ping-Man. “Reinforcement learning in nonstationary environments.” 2000. Web. 19 Oct 2019.

Vancouver:

Choi P. Reinforcement learning in nonstationary environments. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2000. [cited 2019 Oct 19]. Available from: https://doi.org/10.14711/thesis-b655883 ; http://repository.ust.hk/ir/bitstream/1783.1-1522/1/th_redirect.html.

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

Council of Science Editors:

Choi P. Reinforcement learning in nonstationary environments. [Thesis]. Hong Kong University of Science and Technology; 2000. Available from: https://doi.org/10.14711/thesis-b655883 ; http://repository.ust.hk/ir/bitstream/1783.1-1522/1/th_redirect.html

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


Ryerson University

20. Salmon, Ricardo. Reinforcement learning using associative memory networks.

Degree: 2009, Ryerson University

 It is shown that associative memory networks are capable of solving immediate and general reinforcement learning (RL) problems by combining techniques from associative neural networks… (more)

Subjects/Keywords: Neural networks (Computer science); Reinforcement learning (Machine learning); Memory

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

APA (6th Edition):

Salmon, R. (2009). Reinforcement learning using associative memory networks. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A1069

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):

Salmon, Ricardo. “Reinforcement learning using associative memory networks.” 2009. Thesis, Ryerson University. Accessed October 19, 2019. https://digital.library.ryerson.ca/islandora/object/RULA%3A1069.

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

MLA Handbook (7th Edition):

Salmon, Ricardo. “Reinforcement learning using associative memory networks.” 2009. Web. 19 Oct 2019.

Vancouver:

Salmon R. Reinforcement learning using associative memory networks. [Internet] [Thesis]. Ryerson University; 2009. [cited 2019 Oct 19]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1069.

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

Council of Science Editors:

Salmon R. Reinforcement learning using associative memory networks. [Thesis]. Ryerson University; 2009. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1069

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


Oregon State University

21. Ok, DoKyeong. A study of model-based average reward reinforcement learning.

Degree: PhD, Computer Science, 1996, Oregon State University

Reinforcement Learning (RL) is the study of learning agents that improve their performance from rewards and punishments. Most reinforcement learning methods optimize the discounted total… (more)

Subjects/Keywords: Reinforcement learning (Machine learning)

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

APA (6th Edition):

Ok, D. (1996). A study of model-based average reward reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34698

Chicago Manual of Style (16th Edition):

Ok, DoKyeong. “A study of model-based average reward reinforcement learning.” 1996. Doctoral Dissertation, Oregon State University. Accessed October 19, 2019. http://hdl.handle.net/1957/34698.

MLA Handbook (7th Edition):

Ok, DoKyeong. “A study of model-based average reward reinforcement learning.” 1996. Web. 19 Oct 2019.

Vancouver:

Ok D. A study of model-based average reward reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 1996. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1957/34698.

Council of Science Editors:

Ok D. A study of model-based average reward reinforcement learning. [Doctoral Dissertation]. Oregon State University; 1996. Available from: http://hdl.handle.net/1957/34698


Oregon State University

22. Natarajan, Sriraam. Multi-criteria average reward reinforcement learning.

Degree: MS, Computer Science, 2004, Oregon State University

Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving… (more)

Subjects/Keywords: Reinforcement learning (Machine learning)

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APA (6th Edition):

Natarajan, S. (2004). Multi-criteria average reward reinforcement learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/22859

Chicago Manual of Style (16th Edition):

Natarajan, Sriraam. “Multi-criteria average reward reinforcement learning.” 2004. Masters Thesis, Oregon State University. Accessed October 19, 2019. http://hdl.handle.net/1957/22859.

MLA Handbook (7th Edition):

Natarajan, Sriraam. “Multi-criteria average reward reinforcement learning.” 2004. Web. 19 Oct 2019.

Vancouver:

Natarajan S. Multi-criteria average reward reinforcement learning. [Internet] [Masters thesis]. Oregon State University; 2004. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1957/22859.

Council of Science Editors:

Natarajan S. Multi-criteria average reward reinforcement learning. [Masters Thesis]. Oregon State University; 2004. Available from: http://hdl.handle.net/1957/22859


Case Western Reserve University

23. Ewing, Gabriel. Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces.

Degree: MSs, EECS - Computer and Information Sciences, 2018, Case Western Reserve University

 In this thesis, we address the task of reinforcement learning in continuous state and action spaces. Specifically, we consider multi-task reinforcement learning, where a sequence… (more)

Subjects/Keywords: Computer Science; Machine learning; reinforcement learning; continuous actions; knowledge transfer; prostheses

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APA (6th Edition):

Ewing, G. (2018). Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1512748071082221

Chicago Manual of Style (16th Edition):

Ewing, Gabriel. “Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces.” 2018. Masters Thesis, Case Western Reserve University. Accessed October 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1512748071082221.

MLA Handbook (7th Edition):

Ewing, Gabriel. “Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces.” 2018. Web. 19 Oct 2019.

Vancouver:

Ewing G. Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces. [Internet] [Masters thesis]. Case Western Reserve University; 2018. [cited 2019 Oct 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1512748071082221.

Council of Science Editors:

Ewing G. Knowledge Transfer from Expert Demonstrations in Continuous State-Action Spaces. [Masters Thesis]. Case Western Reserve University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1512748071082221


Portland State University

24. Cleland, Andrew Lewis. Bounding Box Improvement with Reinforcement Learning.

Degree: MS(M.S.) in Computer Science, Computer Science, 2018, Portland State University

  In this thesis, I explore a reinforcement learning technique for improving bounding box localizations of objects in images. The model takes as input a… (more)

Subjects/Keywords: Reinforcement learning; Machine learning; Computer vision; Computer Sciences

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APA (6th Edition):

Cleland, A. L. (2018). Bounding Box Improvement with Reinforcement Learning. (Masters Thesis). Portland State University. Retrieved from https://pdxscholar.library.pdx.edu/open_access_etds/4438

Chicago Manual of Style (16th Edition):

Cleland, Andrew Lewis. “Bounding Box Improvement with Reinforcement Learning.” 2018. Masters Thesis, Portland State University. Accessed October 19, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/4438.

MLA Handbook (7th Edition):

Cleland, Andrew Lewis. “Bounding Box Improvement with Reinforcement Learning.” 2018. Web. 19 Oct 2019.

Vancouver:

Cleland AL. Bounding Box Improvement with Reinforcement Learning. [Internet] [Masters thesis]. Portland State University; 2018. [cited 2019 Oct 19]. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/4438.

Council of Science Editors:

Cleland AL. Bounding Box Improvement with Reinforcement Learning. [Masters Thesis]. Portland State University; 2018. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/4438


Leiden University

25. Hoekstra, Freek. Learning Visuomotor Robot Control.

Degree: 2018, Leiden University

 Deep reinforcement learning has solved the game of Go, along with all other board games. Can it also be applied to real-world use cases? This… (more)

Subjects/Keywords: Deep Learning; Robot; Machine Learning; Vision; Control; reinforcement

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APA (6th Edition):

Hoekstra, F. (2018). Learning Visuomotor Robot Control. (Masters Thesis). Leiden University. Retrieved from http://hdl.handle.net/1887/61184

Chicago Manual of Style (16th Edition):

Hoekstra, Freek. “Learning Visuomotor Robot Control.” 2018. Masters Thesis, Leiden University. Accessed October 19, 2019. http://hdl.handle.net/1887/61184.

MLA Handbook (7th Edition):

Hoekstra, Freek. “Learning Visuomotor Robot Control.” 2018. Web. 19 Oct 2019.

Vancouver:

Hoekstra F. Learning Visuomotor Robot Control. [Internet] [Masters thesis]. Leiden University; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1887/61184.

Council of Science Editors:

Hoekstra F. Learning Visuomotor Robot Control. [Masters Thesis]. Leiden University; 2018. Available from: http://hdl.handle.net/1887/61184


Uppsala University

26. Elvira, Boman. Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change.

Degree: Division of Systems and Control, 2018, Uppsala University

  The consequences of climate change are already noticeable in small island developing states. Road networks are crucial for a functioning society, and are particularly… (more)

Subjects/Keywords: Deep reinforcement learning; Climate change adaptation; Machine learning; Computer Engineering; Datorteknik

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APA (6th Edition):

Elvira, B. (2018). Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-373502

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):

Elvira, Boman. “Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change.” 2018. Thesis, Uppsala University. Accessed October 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-373502.

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

MLA Handbook (7th Edition):

Elvira, Boman. “Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change.” 2018. Web. 19 Oct 2019.

Vancouver:

Elvira B. Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change. [Internet] [Thesis]. Uppsala University; 2018. [cited 2019 Oct 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-373502.

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

Council of Science Editors:

Elvira B. Deep Reinforcement Learning for Intelligent Road Maintenance in Small Island Developing States Vulnerable to Climate Change : Using Artificial Intelligence to Adapt Communities to Climate Change. [Thesis]. Uppsala University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-373502

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


University of Waterloo

27. Liang, Jia. Machine Learning for SAT Solvers.

Degree: 2018, University of Waterloo

 Boolean SAT solvers are indispensable tools in a variety of domains in computer science and engineering where efficient search is required. Not only does this… (more)

Subjects/Keywords: Branching heuristic; Restart; Reinforcement learning; Sat solver; Machine learning; Optimization

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

APA (6th Edition):

Liang, J. (2018). Machine Learning for SAT Solvers. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14207

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):

Liang, Jia. “Machine Learning for SAT Solvers.” 2018. Thesis, University of Waterloo. Accessed October 19, 2019. http://hdl.handle.net/10012/14207.

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

MLA Handbook (7th Edition):

Liang, Jia. “Machine Learning for SAT Solvers.” 2018. Web. 19 Oct 2019.

Vancouver:

Liang J. Machine Learning for SAT Solvers. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10012/14207.

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

Council of Science Editors:

Liang J. Machine Learning for SAT Solvers. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/14207

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


University of New Mexico

28. Perez Rodriguez, Juan Samuel. Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach.

Degree: Electrical and Computer Engineering, 2016, University of New Mexico

 The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to… (more)

Subjects/Keywords: machine learning; 5g; user association; load balancing; clustering; reinforcement learning

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

APA (6th Edition):

Perez Rodriguez, J. S. (2016). Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/32977

Chicago Manual of Style (16th Edition):

Perez Rodriguez, Juan Samuel. “Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach.” 2016. Masters Thesis, University of New Mexico. Accessed October 19, 2019. http://hdl.handle.net/1928/32977.

MLA Handbook (7th Edition):

Perez Rodriguez, Juan Samuel. “Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach.” 2016. Web. 19 Oct 2019.

Vancouver:

Perez Rodriguez JS. Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach. [Internet] [Masters thesis]. University of New Mexico; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1928/32977.

Council of Science Editors:

Perez Rodriguez JS. Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach. [Masters Thesis]. University of New Mexico; 2016. Available from: http://hdl.handle.net/1928/32977


University of New South Wales

29. Hengst, Bernhard. Discovering hierarchy in reinforcement learning.

Degree: Computer Science & Engineering, 2003, University of New South Wales

 This thesis addresses the open problem of automatically discovering hierarchical structure in reinforcement learning. Current algorithms for reinforcement learning fail to scale as problems become… (more)

Subjects/Keywords: Reinforcement learning (Machine learning)

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APA (6th Edition):

Hengst, B. (2003). Discovering hierarchy in reinforcement learning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/20497 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:620/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Hengst, Bernhard. “Discovering hierarchy in reinforcement learning.” 2003. Doctoral Dissertation, University of New South Wales. Accessed October 19, 2019. http://handle.unsw.edu.au/1959.4/20497 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:620/SOURCE01?view=true.

MLA Handbook (7th Edition):

Hengst, Bernhard. “Discovering hierarchy in reinforcement learning.” 2003. Web. 19 Oct 2019.

Vancouver:

Hengst B. Discovering hierarchy in reinforcement learning. [Internet] [Doctoral dissertation]. University of New South Wales; 2003. [cited 2019 Oct 19]. Available from: http://handle.unsw.edu.au/1959.4/20497 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:620/SOURCE01?view=true.

Council of Science Editors:

Hengst B. Discovering hierarchy in reinforcement learning. [Doctoral Dissertation]. University of New South Wales; 2003. Available from: http://handle.unsw.edu.au/1959.4/20497 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:620/SOURCE01?view=true


Virginia Tech

30. Kozy, Mark Alexander. Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation.

Degree: MS, Electrical Engineering, 2019, Virginia Tech

 In this paper we address radar-communication coexistence by modelling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize… (more)

Subjects/Keywords: Cognitive radar; machine learning; reinforcement learning; tracking radar; software defined radio

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

APA (6th Edition):

Kozy, M. A. (2019). Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/89948

Chicago Manual of Style (16th Edition):

Kozy, Mark Alexander. “Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation.” 2019. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/89948.

MLA Handbook (7th Edition):

Kozy, Mark Alexander. “Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation.” 2019. Web. 19 Oct 2019.

Vancouver:

Kozy MA. Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/89948.

Council of Science Editors:

Kozy MA. Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/89948

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