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You searched for subject:(hierarchical reinforcement learning). Showing records 1 – 27 of 27 total matches.

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Oregon State University

1. Mehta, Neville. Hierarchical structure discovery and transfer in sequential decision problems.

Degree: PhD, Computer Science, 2011, Oregon State University

 Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or… (more)

Subjects/Keywords: hierarchical reinforcement learning; Reinforcement learning

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APA (6th Edition):

Mehta, N. (2011). Hierarchical structure discovery and transfer in sequential decision problems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25199

Chicago Manual of Style (16th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Doctoral Dissertation, Oregon State University. Accessed July 04, 2020. http://hdl.handle.net/1957/25199.

MLA Handbook (7th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 04 Jul 2020.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1957/25199.

Council of Science Editors:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25199


University of New South Wales

2. 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 July 04, 2020. 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. 04 Jul 2020.

Vancouver:

Harris S. Exploiting Similarity of Structure in Hierarchical Reinforcement Learning with QBOND. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2020 Jul 04]. 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


University of Waterloo

3. Rasmussen, Daniel. Hierarchical reinforcement learning in a biologically plausible neural architecture.

Degree: 2014, University of Waterloo

 Humans and other animals have an impressive ability to quickly adapt to unfamiliar environments, with only minimal feedback. Computational models have been able to provide… (more)

Subjects/Keywords: neural modelling; reinforcement learning; hierarchical reinforcement learning; computational neuroscience

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APA (6th Edition):

Rasmussen, D. (2014). Hierarchical reinforcement learning in a biologically plausible neural architecture. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8943

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):

Rasmussen, Daniel. “Hierarchical reinforcement learning in a biologically plausible neural architecture.” 2014. Thesis, University of Waterloo. Accessed July 04, 2020. http://hdl.handle.net/10012/8943.

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

MLA Handbook (7th Edition):

Rasmussen, Daniel. “Hierarchical reinforcement learning in a biologically plausible neural architecture.” 2014. Web. 04 Jul 2020.

Vancouver:

Rasmussen D. Hierarchical reinforcement learning in a biologically plausible neural architecture. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10012/8943.

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

Council of Science Editors:

Rasmussen D. Hierarchical reinforcement learning in a biologically plausible neural architecture. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8943

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

4. Koupaee, Mahnaz. Abstractive Text Summarization Using Hierarchical Reinforcement Learning.

Degree: 2018, University of California – eScholarship, University of California

 Sequence-to-sequence models have recently gained the state of the art performance in summarization. However, not too many large-scale high-quality datasets are available and almost all… (more)

Subjects/Keywords: Computer science; Abstractive summarization; Hierarchical reinforcement learning; Reinforcement learning; Text summarization

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APA (6th Edition):

Koupaee, M. (2018). Abstractive Text Summarization Using Hierarchical Reinforcement Learning. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/87k3n1rc

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):

Koupaee, Mahnaz. “Abstractive Text Summarization Using Hierarchical Reinforcement Learning.” 2018. Thesis, University of California – eScholarship, University of California. Accessed July 04, 2020. http://www.escholarship.org/uc/item/87k3n1rc.

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

MLA Handbook (7th Edition):

Koupaee, Mahnaz. “Abstractive Text Summarization Using Hierarchical Reinforcement Learning.” 2018. Web. 04 Jul 2020.

Vancouver:

Koupaee M. Abstractive Text Summarization Using Hierarchical Reinforcement Learning. [Internet] [Thesis]. University of California – eScholarship, University of California; 2018. [cited 2020 Jul 04]. Available from: http://www.escholarship.org/uc/item/87k3n1rc.

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

Council of Science Editors:

Koupaee M. Abstractive Text Summarization Using Hierarchical Reinforcement Learning. [Thesis]. University of California – eScholarship, University of California; 2018. Available from: http://www.escholarship.org/uc/item/87k3n1rc

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


Delft University of Technology

5. Sawant, Shambhuraj (author). Hierarchical Reinforcement Learning for Spatio-temporal Planning.

Degree: 2018, Delft University of Technology

Reinforcement learning (RL) is an area of Machine Learning (ML) concerned with learning how a software-defined agent should act in an environment to maximize the… (more)

Subjects/Keywords: Markov Decision Processes; Semi-Markov Decision Processes; Hierarchical Reinforcement Learning; Reinforcement Learning

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APA (6th Edition):

Sawant, S. (. (2018). Hierarchical Reinforcement Learning for Spatio-temporal Planning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5b24c89d-0f5c-40e0-86a6-dd453250860d

Chicago Manual of Style (16th Edition):

Sawant, Shambhuraj (author). “Hierarchical Reinforcement Learning for Spatio-temporal Planning.” 2018. Masters Thesis, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:5b24c89d-0f5c-40e0-86a6-dd453250860d.

MLA Handbook (7th Edition):

Sawant, Shambhuraj (author). “Hierarchical Reinforcement Learning for Spatio-temporal Planning.” 2018. Web. 04 Jul 2020.

Vancouver:

Sawant S(. Hierarchical Reinforcement Learning for Spatio-temporal Planning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:5b24c89d-0f5c-40e0-86a6-dd453250860d.

Council of Science Editors:

Sawant S(. Hierarchical Reinforcement Learning for Spatio-temporal Planning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:5b24c89d-0f5c-40e0-86a6-dd453250860d


Delft University of Technology

6. Hoogvliet, Jonathan (author). Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure.

Degree: 2019, Delft University of Technology

Reinforcement learning (RL) is a model-free adaptive approach to learn a non-linear control law for flight control. However, for flat-RL (FRL) the size of the… (more)

Subjects/Keywords: Hierarchical Reinforcement Learning; Flight Control Systems; Reinforcement Learning; Control; Simulation; Aircraft; sample efficiency; flat reinforcement learning

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

APA (6th Edition):

Hoogvliet, J. (. (2019). Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d66efdb7-d7c7-4c44-9b50-64678ffdf60d

Chicago Manual of Style (16th Edition):

Hoogvliet, Jonathan (author). “Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure.” 2019. Masters Thesis, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:d66efdb7-d7c7-4c44-9b50-64678ffdf60d.

MLA Handbook (7th Edition):

Hoogvliet, Jonathan (author). “Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure.” 2019. Web. 04 Jul 2020.

Vancouver:

Hoogvliet J(. Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:d66efdb7-d7c7-4c44-9b50-64678ffdf60d.

Council of Science Editors:

Hoogvliet J(. Hierarchical Reinforcement Learning for Model-Free Flight Control: A sample efficient tabular approach using Q(lambda)-learning and options in a traditional flight control structure. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d66efdb7-d7c7-4c44-9b50-64678ffdf60d

7. Mahjourian, Reza. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This… (more)

Subjects/Keywords: Robotics; Table tennis; Self-play; Reinforcement learning; Hierarchical policy

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APA (6th Edition):

Mahjourian, R. (2019). Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72812

Chicago Manual of Style (16th Edition):

Mahjourian, Reza. “Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed July 04, 2020. http://hdl.handle.net/2152/72812.

MLA Handbook (7th Edition):

Mahjourian, Reza. “Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.” 2019. Web. 04 Jul 2020.

Vancouver:

Mahjourian R. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/2152/72812.

Council of Science Editors:

Mahjourian R. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72812


University of Georgia

8. 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

9. 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 Rochester

10. Chu, Yi. Intelligent prompting systems for people with cognitive disabilities.

Degree: PhD, 2014, University of Rochester

 The considerable impact that intelligent prompting systems could have on the lives of people with cognitive disabilities is gaining wide interest. Such intelligent systems would… (more)

Subjects/Keywords: Activity recognition; Assistive technology; Cognitive disabilities; Hierarchical reinforcement learning; Intelligent prompting; POMDP

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APA (6th Edition):

Chu, Y. (2014). Intelligent prompting systems for people with cognitive disabilities. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28289

Chicago Manual of Style (16th Edition):

Chu, Yi. “Intelligent prompting systems for people with cognitive disabilities.” 2014. Doctoral Dissertation, University of Rochester. Accessed July 04, 2020. http://hdl.handle.net/1802/28289.

MLA Handbook (7th Edition):

Chu, Yi. “Intelligent prompting systems for people with cognitive disabilities.” 2014. Web. 04 Jul 2020.

Vancouver:

Chu Y. Intelligent prompting systems for people with cognitive disabilities. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1802/28289.

Council of Science Editors:

Chu Y. Intelligent prompting systems for people with cognitive disabilities. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28289


University of California – Merced

11. Rafati Heravi, Jacob. Learning Representations in Reinforcement Learning.

Degree: Electrical Engineering and Computer Science, 2019, University of California – Merced

Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection policy to increase rewarding experiences in their environments. Temporal Difference (TD) learning algorithm,… (more)

Subjects/Keywords: Artificial intelligence; Computer science; Applied mathematics; Hierarchical Representations; Machine Learning; Quasi-Newton Optimization; Reinforcement Learning; Sparse Representations; Trust-Region Optimization

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APA (6th Edition):

Rafati Heravi, J. (2019). Learning Representations in Reinforcement Learning. (Thesis). University of California – Merced. Retrieved from http://www.escholarship.org/uc/item/3dx2f8kq

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):

Rafati Heravi, Jacob. “Learning Representations in Reinforcement Learning.” 2019. Thesis, University of California – Merced. Accessed July 04, 2020. http://www.escholarship.org/uc/item/3dx2f8kq.

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

MLA Handbook (7th Edition):

Rafati Heravi, Jacob. “Learning Representations in Reinforcement Learning.” 2019. Web. 04 Jul 2020.

Vancouver:

Rafati Heravi J. Learning Representations in Reinforcement Learning. [Internet] [Thesis]. University of California – Merced; 2019. [cited 2020 Jul 04]. Available from: http://www.escholarship.org/uc/item/3dx2f8kq.

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

Council of Science Editors:

Rafati Heravi J. Learning Representations in Reinforcement Learning. [Thesis]. University of California – Merced; 2019. Available from: http://www.escholarship.org/uc/item/3dx2f8kq

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


University of Illinois – Urbana-Champaign

12. Davidson, James. Exploiting insensitivity in stochastic systems to learn approximately optimal policies.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 How does uncertainty affect a robot when attempting to generate a control policy to achieve some objective? How sensitive is the obtained control policy to… (more)

Subjects/Keywords: partially observable Markov decision process (POMDP); stochastic systems; sensitivity analysis; reinforcement learning; hierarchical learning methods; spatial abstraction; temporal abstraction

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APA (6th Edition):

Davidson, J. (2012). Exploiting insensitivity in stochastic systems to learn approximately optimal policies. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/34400

Chicago Manual of Style (16th Edition):

Davidson, James. “Exploiting insensitivity in stochastic systems to learn approximately optimal policies.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 04, 2020. http://hdl.handle.net/2142/34400.

MLA Handbook (7th Edition):

Davidson, James. “Exploiting insensitivity in stochastic systems to learn approximately optimal policies.” 2012. Web. 04 Jul 2020.

Vancouver:

Davidson J. Exploiting insensitivity in stochastic systems to learn approximately optimal policies. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/2142/34400.

Council of Science Editors:

Davidson J. Exploiting insensitivity in stochastic systems to learn approximately optimal policies. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/34400


University of British Columbia

13. 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


University of Waterloo

14. Pereira, Sahil. Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments.

Degree: 2020, University of Waterloo

 This thesis explores the application of multi-agent reinforcement learning in domains containing asymmetries between agents, caused by differences in information and position, resulting in a… (more)

Subjects/Keywords: reinforcement learning; multi-agent; stackelberg model; hierarchical environments; game theory; machine learning; continuous space; policy gradient; markov games; actor critic

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APA (6th Edition):

Pereira, S. (2020). Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15851

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):

Pereira, Sahil. “Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments.” 2020. Thesis, University of Waterloo. Accessed July 04, 2020. http://hdl.handle.net/10012/15851.

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

MLA Handbook (7th Edition):

Pereira, Sahil. “Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments.” 2020. Web. 04 Jul 2020.

Vancouver:

Pereira S. Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/10012/15851.

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

Council of Science Editors:

Pereira S. Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15851

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


Delft University of Technology

15. Mannucci, T. Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles.

Degree: 2017, Delft University of Technology

Subjects/Keywords: Unmanned Aerial Vehicles; Reinforcement Learning; Safe Exploration; Hierarchical Reinforcement Learning

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APA (6th Edition):

Mannucci, T. (2017). Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; dbaf67cc-598c-4b26-b07f-5d781722ebfd ; 10.4233/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:isbn:978-94-028-0762-2 ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd

Chicago Manual of Style (16th Edition):

Mannucci, T. “Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles.” 2017. Doctoral Dissertation, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; dbaf67cc-598c-4b26-b07f-5d781722ebfd ; 10.4233/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:isbn:978-94-028-0762-2 ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd.

MLA Handbook (7th Edition):

Mannucci, T. “Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles.” 2017. Web. 04 Jul 2020.

Vancouver:

Mannucci T. Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles. [Internet] [Doctoral dissertation]. Delft University of Technology; 2017. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; dbaf67cc-598c-4b26-b07f-5d781722ebfd ; 10.4233/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:isbn:978-94-028-0762-2 ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd.

Council of Science Editors:

Mannucci T. Safe Online Robust Exploration for Reinforcement Learning Control of Unmanned Aerial Vehicles. [Doctoral Dissertation]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; dbaf67cc-598c-4b26-b07f-5d781722ebfd ; 10.4233/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; urn:isbn:978-94-028-0762-2 ; urn:NBN:nl:ui:24-uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd ; http://resolver.tudelft.nl/uuid:dbaf67cc-598c-4b26-b07f-5d781722ebfd


University of Toronto

16. Doroodgar, Barzin. A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments.

Degree: 2011, University of Toronto

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and… (more)

Subjects/Keywords: rescue robot; urban search and rescue; hierarchical reinforcement learning; semi-autonomous control; robot exploration; control architecture; 0771; 0800; 0548

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APA (6th Edition):

Doroodgar, B. (2011). A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/30576

Chicago Manual of Style (16th Edition):

Doroodgar, Barzin. “A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments.” 2011. Masters Thesis, University of Toronto. Accessed July 04, 2020. http://hdl.handle.net/1807/30576.

MLA Handbook (7th Edition):

Doroodgar, Barzin. “A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments.” 2011. Web. 04 Jul 2020.

Vancouver:

Doroodgar B. A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments. [Internet] [Masters thesis]. University of Toronto; 2011. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1807/30576.

Council of Science Editors:

Doroodgar B. A Learning-based Semi-autonomous Control Architecture for Robotic Exploration in Search and Rescue Environments. [Masters Thesis]. University of Toronto; 2011. Available from: http://hdl.handle.net/1807/30576


Brno University of Technology

17. Krušina, Jan. Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment.

Degree: 2019, Brno University of Technology

 The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning(more)

Subjects/Keywords: posilované učení; hierarchické posilované učení; Starcraft II; PySC2; hluboké neuronové sítě; DeepMind; RTS; strategické hry v reálném čase; učení s učitelem; Tensorflow; hluboké učení; reinforcement learning; hierarchical reinforcement learning; Starcraft II; PySC2; deep neural networks; DeepMind; RTS; real-time strategy games; supervised learning; Tensorflow; deep learning

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APA (6th Edition):

Krušina, J. (2019). Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/84866

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):

Krušina, Jan. “Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment.” 2019. Thesis, Brno University of Technology. Accessed July 04, 2020. http://hdl.handle.net/11012/84866.

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

MLA Handbook (7th Edition):

Krušina, Jan. “Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment.” 2019. Web. 04 Jul 2020.

Vancouver:

Krušina J. Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/11012/84866.

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

Council of Science Editors:

Krušina J. Zlepšování systému pro automatické hraní hry Starcraft II v prostředí PySC2: Improving Bots Playing Starcraft II Game in PySC2 Environment. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/84866

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


Delft University of Technology

18. Junell, J.L. An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles.

Degree: 2018, Delft University of Technology

 The use of Micro Aerial Vehicles (MAVs) in practical applications, to solve real-world problems, is growing in demand as the technology becomes more widely known… (more)

Subjects/Keywords: Reinforcement Learning; Micro Aerial Vehicle; Quadrotor; Policy Iteration; Hierarchical Reinforcement Learning; State Abstraction; Transfer learning

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

APA (6th Edition):

Junell, J. L. (2018). An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294

Chicago Manual of Style (16th Edition):

Junell, J L. “An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles.” 2018. Doctoral Dissertation, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294.

MLA Handbook (7th Edition):

Junell, J L. “An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles.” 2018. Web. 04 Jul 2020.

Vancouver:

Junell JL. An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles. [Internet] [Doctoral dissertation]. Delft University of Technology; 2018. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294.

Council of Science Editors:

Junell JL. An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles. [Doctoral Dissertation]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294

19. Irani, Arya John. Utilizing negative policy information to accelerate reinforcement learning.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 A pilot study by Subramanian et al. on Markov decision problem task decomposition by humans revealed that participants break down tasks into both short-term subgoals… (more)

Subjects/Keywords:

…has been studied in the literature within the field of Hierarchical Reinforcement Learning… …participants’ exemplars. . . . 61 viii LIST OF FIGURES 1 The reinforcement learning framework… …information improves reinforcement learning performance by decreasing the amount of training needed… …to utilize those human insights in reinforcement learning. A pilot study by Subramanian et… …accelerates reinforcement learning by decreasing the number of learning episodes needed to reach a… 

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

APA (6th Edition):

Irani, A. J. (2015). Utilizing negative policy information to accelerate reinforcement learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53481

Chicago Manual of Style (16th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed July 04, 2020. http://hdl.handle.net/1853/53481.

MLA Handbook (7th Edition):

Irani, Arya John. “Utilizing negative policy information to accelerate reinforcement learning.” 2015. Web. 04 Jul 2020.

Vancouver:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1853/53481.

Council of Science Editors:

Irani AJ. Utilizing negative policy information to accelerate reinforcement learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53481

20. LI ZHUORU. EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING.

Degree: 2015, National University of Singapore

Subjects/Keywords: hierarchical reinforcement learning; Markov decision process; seqential decision making; options; MAXQ

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APA (6th Edition):

ZHUORU, L. (2015). EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/122593

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):

ZHUORU, LI. “EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING.” 2015. Thesis, National University of Singapore. Accessed July 04, 2020. http://scholarbank.nus.edu.sg/handle/10635/122593.

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

MLA Handbook (7th Edition):

ZHUORU, LI. “EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING.” 2015. Web. 04 Jul 2020.

Vancouver:

ZHUORU L. EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING. [Internet] [Thesis]. National University of Singapore; 2015. [cited 2020 Jul 04]. Available from: http://scholarbank.nus.edu.sg/handle/10635/122593.

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

Council of Science Editors:

ZHUORU L. EFFICIENT HIERARCHICAL REINFORCEMENT LEARNING THROUGH CORE TASK ABSTRACTION AND CONTEXT REASONING. [Thesis]. National University of Singapore; 2015. Available from: http://scholarbank.nus.edu.sg/handle/10635/122593

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


University of Toronto

21. Chan, Jeanie. A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions.

Degree: 2011, University of Toronto

Due to the world’s rapidly growing elderly population, dementia is becoming increasingly prevalent. This poses considerable health, social, and economic concerns as it impacts individuals,… (more)

Subjects/Keywords: Human-robot Interaction; Control Architecture; Robotics; Hierarchical Reinforcement Learning; Multi-modal Human-machine Interfaces; Cognitive Interventions; Socially Assistive Robots; 0771; 0800; 0548

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

APA (6th Edition):

Chan, J. (2011). A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/30536

Chicago Manual of Style (16th Edition):

Chan, Jeanie. “A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions.” 2011. Masters Thesis, University of Toronto. Accessed July 04, 2020. http://hdl.handle.net/1807/30536.

MLA Handbook (7th Edition):

Chan, Jeanie. “A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions.” 2011. Web. 04 Jul 2020.

Vancouver:

Chan J. A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions. [Internet] [Masters thesis]. University of Toronto; 2011. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1807/30536.

Council of Science Editors:

Chan J. A Learning-based Control Architecture for Socially Assistive Robots Providing Cognitive Interventions. [Masters Thesis]. University of Toronto; 2011. Available from: http://hdl.handle.net/1807/30536


University of Edinburgh

22. Cuayáhuitl, Heriberto. Hierarchical reinforcement learning for spoken dialogue systems.

Degree: PhD, 2009, University of Edinburgh

 This thesis focuses on the problem of scalable optimization of dialogue behaviour in speech-based conversational systems using reinforcement learning. Most previous investigations in dialogue strategy… (more)

Subjects/Keywords: 020; Spoken dialogue systems; (Semi-) Automatic dialogue strategy design; Hierarchical control; Prior expert knowledge; Semi-Markov decision processes; Hierarchical reinforcement learning

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

APA (6th Edition):

Cuayáhuitl, H. (2009). Hierarchical reinforcement learning for spoken dialogue systems. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/2750

Chicago Manual of Style (16th Edition):

Cuayáhuitl, Heriberto. “Hierarchical reinforcement learning for spoken dialogue systems.” 2009. Doctoral Dissertation, University of Edinburgh. Accessed July 04, 2020. http://hdl.handle.net/1842/2750.

MLA Handbook (7th Edition):

Cuayáhuitl, Heriberto. “Hierarchical reinforcement learning for spoken dialogue systems.” 2009. Web. 04 Jul 2020.

Vancouver:

Cuayáhuitl H. Hierarchical reinforcement learning for spoken dialogue systems. [Internet] [Doctoral dissertation]. University of Edinburgh; 2009. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1842/2750.

Council of Science Editors:

Cuayáhuitl H. Hierarchical reinforcement learning for spoken dialogue systems. [Doctoral Dissertation]. University of Edinburgh; 2009. Available from: http://hdl.handle.net/1842/2750


Delft University of Technology

23. Zhou, Y. Online reinforcement learning control for aerospace systems.

Degree: 2018, Delft University of Technology

Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve… (more)

Subjects/Keywords: Reinforcement Learning; Aerospace Systems; Optimal Adaptive Control; Approximate Dynamic Programming; Adaptive Critic Designs; Incremental Model; Nonlinear Systems; Partial Observability; Hierarchical Reinforcement Learning; HybridMethods

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

APA (6th Edition):

Zhou, Y. (2018). Online reinforcement learning control for aerospace systems. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; 5b875915-2518-4ec8-a1a0-07ad057edab4 ; 10.4233/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:isbn:978-94-6366-021-1 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4

Chicago Manual of Style (16th Edition):

Zhou, Y. “Online reinforcement learning control for aerospace systems.” 2018. Doctoral Dissertation, Delft University of Technology. Accessed July 04, 2020. http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; 5b875915-2518-4ec8-a1a0-07ad057edab4 ; 10.4233/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:isbn:978-94-6366-021-1 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4.

MLA Handbook (7th Edition):

Zhou, Y. “Online reinforcement learning control for aerospace systems.” 2018. Web. 04 Jul 2020.

Vancouver:

Zhou Y. Online reinforcement learning control for aerospace systems. [Internet] [Doctoral dissertation]. Delft University of Technology; 2018. [cited 2020 Jul 04]. Available from: http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; 5b875915-2518-4ec8-a1a0-07ad057edab4 ; 10.4233/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:isbn:978-94-6366-021-1 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4.

Council of Science Editors:

Zhou Y. Online reinforcement learning control for aerospace systems. [Doctoral Dissertation]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; 5b875915-2518-4ec8-a1a0-07ad057edab4 ; 10.4233/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; urn:isbn:978-94-6366-021-1 ; urn:NBN:nl:ui:24-uuid:5b875915-2518-4ec8-a1a0-07ad057edab4 ; http://resolver.tudelft.nl/uuid:5b875915-2518-4ec8-a1a0-07ad057edab4

24. Binz, Marcel. Learning Goal-Directed Behaviour.

Degree: RPL, 2017, KTH

Learning behaviour of artificial agents is commonly studied in the framework of Reinforcement Learning. Reinforcement Learning gained increasing popularity in the past years. This… (more)

Subjects/Keywords: Hierarchical Reinforcement Learning; Options; Deep Neural Networks; Computer Sciences; Datavetenskap (datalogi)

…papers. 2.1 Feudal Reinforcement Learning Hierarchical Reinforcement Learning is an area of… …hierarchical Reinforcement Learning besides those, that are based on sub-goals and options. The MAXQ… …conclude this chapter with a section on hierarchical Reinforcement Learning. For this we will… …reduced modifications. CHAPTER 3. PRELIMINARIES 3.3 19 Hierarchical Reinforcement Learning… …Hierarchical Reinforcement Learning describes a set of methods, that attempt to extend standard RL… 

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

APA (6th Edition):

Binz, M. (2017). Learning Goal-Directed Behaviour. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213015

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):

Binz, Marcel. “Learning Goal-Directed Behaviour.” 2017. Thesis, KTH. Accessed July 04, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213015.

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

MLA Handbook (7th Edition):

Binz, Marcel. “Learning Goal-Directed Behaviour.” 2017. Web. 04 Jul 2020.

Vancouver:

Binz M. Learning Goal-Directed Behaviour. [Internet] [Thesis]. KTH; 2017. [cited 2020 Jul 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213015.

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

Council of Science Editors:

Binz M. Learning Goal-Directed Behaviour. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213015

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

25. Sodhani, Shagun. Learning competitive ensemble of information-constrained primitives .

Degree: 2019, Université de Montréal

Subjects/Keywords: Reinforcement Learning; Hierarchical Reinforcement Learning; Information Bottleneck; Compositionality; Modular network; Apprentissage par renforcement; Apprentissage par renforcement hiérarchique; Goulot d'étranglement de l'information; Compositionnalité; Réseaux modulaires

…HRL Hierarchical Reinforcement Learning LSTM Long-Short Term Memory MDP Markov Decision… …Hierarchical Reinforcement Learning. We review this body of literature in section 1.3 and describe… …entire state space. In the case of Hierarchical Reinforcement Learning, the primitives have to… …beyond Hierarchical Reinforcement Learning and is studied under the paradigm of Neural Modular… …at architecture designs like hierarchical reinforcement learning (section 1.3)… 

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

APA (6th Edition):

Sodhani, S. (2019). Learning competitive ensemble of information-constrained primitives . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/22537

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):

Sodhani, Shagun. “Learning competitive ensemble of information-constrained primitives .” 2019. Thesis, Université de Montréal. Accessed July 04, 2020. http://hdl.handle.net/1866/22537.

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

MLA Handbook (7th Edition):

Sodhani, Shagun. “Learning competitive ensemble of information-constrained primitives .” 2019. Web. 04 Jul 2020.

Vancouver:

Sodhani S. Learning competitive ensemble of information-constrained primitives . [Internet] [Thesis]. Université de Montréal; 2019. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1866/22537.

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

Council of Science Editors:

Sodhani S. Learning competitive ensemble of information-constrained primitives . [Thesis]. Université de Montréal; 2019. Available from: http://hdl.handle.net/1866/22537

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

26. Heyder, Jakob. Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria.

Degree: computer science and media technology (CM), 2018, Linnaeus University

  Artificial general intelligence is not well defined, but attempts such as the recent listof “Ingredients for building machines that think and learn like humans”… (more)

Subjects/Keywords: General Artificial Intelligence; Machine Learning; Hierarchical Temporal Memory; Autonomous Agent; Reinforcement Learning; Temporal Differ- ence Learning; Human-like Thinking and Learning; Engineering and Technology; Teknik och teknologier

…combination of existing NUPIC HTM technology with Reinforcement Learning (RL). The agent… …combination of unsupervised learning and Reinforcement Learning(RL), intelligent software… …combination of deep learning and RL - deep reinforcement learning - to develop software agents that… …learning-to-learn. On the other hand - Numenta, a silicon valley based company - tries to… …understand the neocortex and use this knowledge to build AI-Systems. Numentas Hierarchical… 

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

APA (6th Edition):

Heyder, J. (2018). Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75868

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):

Heyder, Jakob. “Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria.” 2018. Thesis, Linnaeus University. Accessed July 04, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75868.

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

MLA Handbook (7th Edition):

Heyder, Jakob. “Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria.” 2018. Web. 04 Jul 2020.

Vancouver:

Heyder J. Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria. [Internet] [Thesis]. Linnaeus University; 2018. [cited 2020 Jul 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75868.

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

Council of Science Editors:

Heyder J. Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria. [Thesis]. Linnaeus University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75868

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

27. Léonard, Nicholas. Distributed conditional computation .

Degree: 2015, Université de Montréal

 L'objectif de cette thèse est de présenter différentes applications du programme de recherche de calcul conditionnel distribué. On espère que ces applications, ainsi que la… (more)

Subjects/Keywords: calcul conditionnel distribué; réseau de neurones; apprentissage profond; apprentissage supervisé; apprentissage par renforcement; arbres de décisions; modèle de langage; softmax hierarchique; mélange d'experts; torch; distributed conditional computation; neural network; deep learning; supervised learning; reinforcement learning; decision tree; language model; hierarchical softmax; mixture of experts; torch

Reinforcement Learning Global Contrast Normalization Graphics Processing Unit Hierarchical Log… …Unsupervised Learning : roughly speaking, model P (X) ; – Reinforcement Learning : maximize… …x29;. Reinforcement learning is very different from the above two approaches. In this scheme… …Conditional Computation Deep Learning Expectation Maximization Equanimous Sparse Supervised… …Adaptatifs Language Model Maximum a posteriori Machine Learning Multi-Layer Perceptron Mixture of… 

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

APA (6th Edition):

Léonard, N. (2015). Distributed conditional computation . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/11954

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):

Léonard, Nicholas. “Distributed conditional computation .” 2015. Thesis, Université de Montréal. Accessed July 04, 2020. http://hdl.handle.net/1866/11954.

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

MLA Handbook (7th Edition):

Léonard, Nicholas. “Distributed conditional computation .” 2015. Web. 04 Jul 2020.

Vancouver:

Léonard N. Distributed conditional computation . [Internet] [Thesis]. Université de Montréal; 2015. [cited 2020 Jul 04]. Available from: http://hdl.handle.net/1866/11954.

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

Council of Science Editors:

Léonard N. Distributed conditional computation . [Thesis]. Université de Montréal; 2015. Available from: http://hdl.handle.net/1866/11954

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

.