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You searched for subject:(Modular inverse reinforcement learning). Showing records 1 – 30 of 53291 total matches.

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University of Texas – Austin

1. -8073-3276. Parameterized modular inverse reinforcement learning.

Degree: MSin Computer Sciences, Computer Science, 2015, University of Texas – Austin

Reinforcement learning and inverse reinforcement learning can be used to model and understand human behaviors. However, due to the curse of dimensionality, their use as… (more)

Subjects/Keywords: Reinforcement learning; Artificial intelligence; Inverse reinforcement learning; Modular inverse reinforcement learning; Reinforcement learning algorithms; Human navigation behaviors

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

APA (6th Edition):

-8073-3276. (2015). Parameterized modular inverse reinforcement learning. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Masters Thesis, University of Texas – Austin. Accessed December 15, 2019. http://hdl.handle.net/2152/46987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Web. 15 Dec 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8073-3276. Parameterized modular inverse reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2152/46987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-8073-3276. Parameterized modular inverse reinforcement learning. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/46987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Rice University

2. Daptardar, Saurabh. The Science of Mind Reading: New Inverse Optimal Control Framework.

Degree: MS, Engineering, 2018, Rice University

 Continuous control and planning by the brain remain poorly understood and is a major challenge in the field of Neuroscience. To truly say that we… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Inverse Optimal Control; Reinforcement Learning; Optimal Control; Neuroscience

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APA (6th Edition):

Daptardar, S. (2018). The Science of Mind Reading: New Inverse Optimal Control Framework. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105893

Chicago Manual of Style (16th Edition):

Daptardar, Saurabh. “The Science of Mind Reading: New Inverse Optimal Control Framework.” 2018. Masters Thesis, Rice University. Accessed December 15, 2019. http://hdl.handle.net/1911/105893.

MLA Handbook (7th Edition):

Daptardar, Saurabh. “The Science of Mind Reading: New Inverse Optimal Control Framework.” 2018. Web. 15 Dec 2019.

Vancouver:

Daptardar S. The Science of Mind Reading: New Inverse Optimal Control Framework. [Internet] [Masters thesis]. Rice University; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1911/105893.

Council of Science Editors:

Daptardar S. The Science of Mind Reading: New Inverse Optimal Control Framework. [Masters Thesis]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105893


NSYSU

3. Tseng, Yi-Chia. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.

Degree: Master, Electrical Engineering, 2015, NSYSU

Reinforcement learning (RL) techniques use a reward function to correct a learning agent to solve sequential decision making problems through interactions with a dynamic environment,… (more)

Subjects/Keywords: Apprenticeship Learning; Feature weight; Inverse Reinforcement learning; Reward function; Reinforcement learning

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APA (6th Edition):

Tseng, Y. (2015). An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

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):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Thesis, NSYSU. Accessed December 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

MLA Handbook (7th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Web. 15 Dec 2019.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Dec 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

Council of Science Editors:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

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


NSYSU

4. Lin, Hung-shyuan. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.

Degree: Master, Electrical Engineering, 2015, NSYSU

 Itâs a study on Reinforcement Learning, learning interaction of agents and dynamic environment to get reward function R, and update the policy, converge learning and… (more)

Subjects/Keywords: Inverse reinforcement learning; Reward function; Fuzzy; Reinforcement learning; AdaBoost; Apprenticeship learning

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

APA (6th Edition):

Lin, H. (2015). Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

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):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed December 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

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

MLA Handbook (7th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Web. 15 Dec 2019.

Vancouver:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Dec 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

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

Council of Science Editors:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

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


University of Illinois – Chicago

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

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 December 15, 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. 15 Dec 2019.

Vancouver:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Dec 15]. 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


University of Illinois – Chicago

6. Chen, Xiangli. Robust Structured Prediction for Process Data.

Degree: 2017, University of Illinois – Chicago

 Processes involve a series of actions performed to achieve a particular result. Developing prediction models for process data is important for many real problems such… (more)

Subjects/Keywords: Structured Prediction; Optimal Control; Reinforcement Learning; Inverse Reinforcement Learning; Imitation Learning; Regression; Covariate Shift

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APA (6th Edition):

Chen, X. (2017). Robust Structured Prediction for Process Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21987

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):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Thesis, University of Illinois – Chicago. Accessed December 15, 2019. http://hdl.handle.net/10027/21987.

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

MLA Handbook (7th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Web. 15 Dec 2019.

Vancouver:

Chen X. Robust Structured Prediction for Process Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10027/21987.

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

Council of Science Editors:

Chen X. Robust Structured Prediction for Process Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21987

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


NSYSU

7. Cheng, Tien-yu. Inverse Reinforcement Learning based on Critical State.

Degree: Master, Electrical Engineering, 2014, NSYSU

Reinforcement Learning (RL) makes an agent learn through interacting with a dynamic environment. One fundamental assumption of existing RL algorithms is that reward function, the… (more)

Subjects/Keywords: reward feature construction; Apprenticeship Learning; Inverse Reinforcement learning; reward function; Reinforcement learning

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

APA (6th Edition):

Cheng, T. (2014). Inverse Reinforcement Learning based on Critical State. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500

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):

Cheng, Tien-yu. “Inverse Reinforcement Learning based on Critical State.” 2014. Thesis, NSYSU. Accessed December 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500.

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

MLA Handbook (7th Edition):

Cheng, Tien-yu. “Inverse Reinforcement Learning based on Critical State.” 2014. Web. 15 Dec 2019.

Vancouver:

Cheng T. Inverse Reinforcement Learning based on Critical State. [Internet] [Thesis]. NSYSU; 2014. [cited 2019 Dec 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500.

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

Council of Science Editors:

Cheng T. Inverse Reinforcement Learning based on Critical State. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500

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


University of Southern California

8. 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 December 15, 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. 15 Dec 2019.

Vancouver:

Kalakrishnan M. Learning objective functions for autonomous motion generation. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Dec 15]. 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


University of New South Wales

9. Nguyen, Hung. Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles.

Degree: Engineering & Information Technology, 2018, University of New South Wales

 Apprenticeship Learning (AL) uses data collected from humans on tasks to design machine-learning algorithms to imitate the skills used by humans. Such a powerful approach… (more)

Subjects/Keywords: Apprenticeship Learning; Reinforcement learning; Inverse Reinforcement Learning; Apprenticeship Boostrapping; UAV and UGVs

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APA (6th Edition):

Nguyen, H. (2018). Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Nguyen, Hung. “Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles.” 2018. Masters Thesis, University of New South Wales. Accessed December 15, 2019. http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true.

MLA Handbook (7th Edition):

Nguyen, Hung. “Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles.” 2018. Web. 15 Dec 2019.

Vancouver:

Nguyen H. Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles. [Internet] [Masters thesis]. University of New South Wales; 2018. [cited 2019 Dec 15]. Available from: http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true.

Council of Science Editors:

Nguyen H. Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles. [Masters Thesis]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true


NSYSU

10. Chiang, Hsuan-yi. Action Segmentation and Learning by Inverse Reinforcement Learning.

Degree: Master, Electrical Engineering, 2015, NSYSU

Reinforcement learning allows agents to learn behaviors through trial and error. However, as the level of difficulty increases, the reward function of the mission also… (more)

Subjects/Keywords: Upper Confidence Bounds; Adaboost classifier; reward function; Inverse Reinforcement learning; Reinforcement learning

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APA (6th Edition):

Chiang, H. (2015). Action Segmentation and Learning by Inverse Reinforcement Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230

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):

Chiang, Hsuan-yi. “Action Segmentation and Learning by Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed December 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230.

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

MLA Handbook (7th Edition):

Chiang, Hsuan-yi. “Action Segmentation and Learning by Inverse Reinforcement Learning.” 2015. Web. 15 Dec 2019.

Vancouver:

Chiang H. Action Segmentation and Learning by Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Dec 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230.

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

Council of Science Editors:

Chiang H. Action Segmentation and Learning by Inverse Reinforcement Learning. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230

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

11. Das, Indrajit. Inverse reinforcement learning of risk-sensitive utility.

Degree: MS, Computer Science, 2016, University of Georgia

 The uncertain and stochastic nature of the real world poses a challenge for autonomous cars in making decisions to ensure appropriate motion, considering the safety… (more)

Subjects/Keywords: Inverse Reinforcement Learning

…9 2.3 Inverse Reinforcement Learning… …decision makers using cumulative rewards. We apply Inverse Reinforcement Learning (IRL)… …decision making in self-driving cars. 2 We have developed an Inverse Reinforcement Learning (… …function. 3 We are validating the Inverse Reinforcement Learning algorithm with the help of a… …Theory and Inverse Reinforcement Learning, which are the building blocks of both the problem… 

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

APA (6th Edition):

Das, I. (2016). Inverse reinforcement learning of risk-sensitive utility. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/das_indrajit_201608_ms

Chicago Manual of Style (16th Edition):

Das, Indrajit. “Inverse reinforcement learning of risk-sensitive utility.” 2016. Masters Thesis, University of Georgia. Accessed December 15, 2019. http://purl.galileo.usg.edu/uga_etd/das_indrajit_201608_ms.

MLA Handbook (7th Edition):

Das, Indrajit. “Inverse reinforcement learning of risk-sensitive utility.” 2016. Web. 15 Dec 2019.

Vancouver:

Das I. Inverse reinforcement learning of risk-sensitive utility. [Internet] [Masters thesis]. University of Georgia; 2016. [cited 2019 Dec 15]. Available from: http://purl.galileo.usg.edu/uga_etd/das_indrajit_201608_ms.

Council of Science Editors:

Das I. Inverse reinforcement learning of risk-sensitive utility. [Masters Thesis]. University of Georgia; 2016. Available from: http://purl.galileo.usg.edu/uga_etd/das_indrajit_201608_ms

12. Trivedi, Maulesh. Inverse learning of robot behavior for ad-hoc teamwork.

Degree: MS, Artificial Intelligence, 2016, University of Georgia

 Machine Learning and Robotics present a very intriguing combination of research in Artificial Intelligence. Inverse Reinforcement Learning (IRL) algorithms have generated a great deal of… (more)

Subjects/Keywords: Inverse Reinforcement Learning

…Motivation for Inverse Reinforcement Learning Now that we have described a way of modelling… …6 Thus, our research addresses the problem of Inverse Reinforcement Learning (IRL… …conventional reinforcement learning techniques become mute in these situations. Inverse Reinforcement… …have not been developed to work with multi-agent inverse reinforcement learning domains… …task [18] [20] but do not use the inverse reinforcement learning… 

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

APA (6th Edition):

Trivedi, M. (2016). Inverse learning of robot behavior for ad-hoc teamwork. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/trivedi_maulesh_201608_ms

Chicago Manual of Style (16th Edition):

Trivedi, Maulesh. “Inverse learning of robot behavior for ad-hoc teamwork.” 2016. Masters Thesis, University of Georgia. Accessed December 15, 2019. http://purl.galileo.usg.edu/uga_etd/trivedi_maulesh_201608_ms.

MLA Handbook (7th Edition):

Trivedi, Maulesh. “Inverse learning of robot behavior for ad-hoc teamwork.” 2016. Web. 15 Dec 2019.

Vancouver:

Trivedi M. Inverse learning of robot behavior for ad-hoc teamwork. [Internet] [Masters thesis]. University of Georgia; 2016. [cited 2019 Dec 15]. Available from: http://purl.galileo.usg.edu/uga_etd/trivedi_maulesh_201608_ms.

Council of Science Editors:

Trivedi M. Inverse learning of robot behavior for ad-hoc teamwork. [Masters Thesis]. University of Georgia; 2016. Available from: http://purl.galileo.usg.edu/uga_etd/trivedi_maulesh_201608_ms

13. Paraskevopoulos, Vasileios. Design of optimal neural network control strategies with minimal a priori knowledge.

Degree: PhD, 2000, University of Sussex

Subjects/Keywords: 629.8; Reinforcement learning; Real time; Modular

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APA (6th Edition):

Paraskevopoulos, V. (2000). Design of optimal neural network control strategies with minimal a priori knowledge. (Doctoral Dissertation). University of Sussex. Retrieved from https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189

Chicago Manual of Style (16th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Doctoral Dissertation, University of Sussex. Accessed December 15, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189.

MLA Handbook (7th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Web. 15 Dec 2019.

Vancouver:

Paraskevopoulos V. Design of optimal neural network control strategies with minimal a priori knowledge. [Internet] [Doctoral dissertation]. University of Sussex; 2000. [cited 2019 Dec 15]. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189.

Council of Science Editors:

Paraskevopoulos V. Design of optimal neural network control strategies with minimal a priori knowledge. [Doctoral Dissertation]. University of Sussex; 2000. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189


NSYSU

14. Chen, Yan-heng. Analysis of Another Left Shift Binary GCD Algorithm.

Degree: Master, Computer Science and Engineering, 2009, NSYSU

 In general, to compute the modular inverse is very important in information security, many encrypt/decrypt and signature algorithms always need to use it. In 2007,… (more)

Subjects/Keywords: Self-test; Modular inverse; GCD

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APA (6th Edition):

Chen, Y. (2009). Analysis of Another Left Shift Binary GCD Algorithm. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741

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):

Chen, Yan-heng. “Analysis of Another Left Shift Binary GCD Algorithm.” 2009. Thesis, NSYSU. Accessed December 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741.

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

MLA Handbook (7th Edition):

Chen, Yan-heng. “Analysis of Another Left Shift Binary GCD Algorithm.” 2009. Web. 15 Dec 2019.

Vancouver:

Chen Y. Analysis of Another Left Shift Binary GCD Algorithm. [Internet] [Thesis]. NSYSU; 2009. [cited 2019 Dec 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741.

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

Council of Science Editors:

Chen Y. Analysis of Another Left Shift Binary GCD Algorithm. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741

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


University of Oxford

15. Wulfmeier, Markus. Efficient supervision for robot learning via imitation, simulation, and adaptation.

Degree: PhD, 2018, University of Oxford

 In order to enable more widespread application of robots, we are required to reduce the human effort for the introduction of existing robotic platforms to… (more)

Subjects/Keywords: Machine learning; Robotics; Domain Adaptation; Imitation Learning; Inverse Reinforcement Learning; Mobile Robotics; Transfer Learning; Autonomous Driving

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

APA (6th Edition):

Wulfmeier, M. (2018). Efficient supervision for robot learning via imitation, simulation, and adaptation. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819

Chicago Manual of Style (16th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Doctoral Dissertation, University of Oxford. Accessed December 15, 2019. http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

MLA Handbook (7th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Web. 15 Dec 2019.

Vancouver:

Wulfmeier M. Efficient supervision for robot learning via imitation, simulation, and adaptation. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2019 Dec 15]. Available from: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

Council of Science Editors:

Wulfmeier M. Efficient supervision for robot learning via imitation, simulation, and adaptation. [Doctoral Dissertation]. University of Oxford; 2018. Available from: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819

16. Chandramohan, Senthilkumar. Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?.

Degree: Docteur es, Informatique, 2012, Avignon

Les récents progrès dans le domaine du traitement du langage ont apporté un intérêt significatif à la mise en oeuvre de systèmes de dialogue parlé.… (more)

Subjects/Keywords: Simulation d'utilisateurs; Systèmes de dialogue parlé; Apprentissage par renforcement; Apprentissage par renforcement inverse; Gestion de dialogue; User simulation; Spoken dialogue systems; Reinforcement learning; Inverse reinforcement learning; Dialogue management

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

APA (6th Edition):

Chandramohan, S. (2012). Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?. (Doctoral Dissertation). Avignon. Retrieved from http://www.theses.fr/2012AVIG0185

Chicago Manual of Style (16th Edition):

Chandramohan, Senthilkumar. “Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?.” 2012. Doctoral Dissertation, Avignon. Accessed December 15, 2019. http://www.theses.fr/2012AVIG0185.

MLA Handbook (7th Edition):

Chandramohan, Senthilkumar. “Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?.” 2012. Web. 15 Dec 2019.

Vancouver:

Chandramohan S. Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?. [Internet] [Doctoral dissertation]. Avignon; 2012. [cited 2019 Dec 15]. Available from: http://www.theses.fr/2012AVIG0185.

Council of Science Editors:

Chandramohan S. Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?. [Doctoral Dissertation]. Avignon; 2012. Available from: http://www.theses.fr/2012AVIG0185


University of Georgia

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

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 December 15, 2019. http://hdl.handle.net/10724/37273.

MLA Handbook (7th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Web. 15 Dec 2019.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Dec 15]. 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

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

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 December 15, 2019. http://hdl.handle.net/10724/37116.

MLA Handbook (7th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2017. Web. 15 Dec 2019.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Dec 15]. 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


Virginia Tech

19. Shiraev, Dmitry Eric. Inverse Reinforcement Learning and Routing Metric Discovery.

Degree: MS, Computer Science, 2003, Virginia Tech

 Uncovering the metrics and procedures employed by an autonomous networking system is an important problem with applications in instrumentation, traffic engineering, and game-theoretic studies of… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Routing; Network Metrics

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

APA (6th Edition):

Shiraev, D. E. (2003). Inverse Reinforcement Learning and Routing Metric Discovery. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34728

Chicago Manual of Style (16th Edition):

Shiraev, Dmitry Eric. “Inverse Reinforcement Learning and Routing Metric Discovery.” 2003. Masters Thesis, Virginia Tech. Accessed December 15, 2019. http://hdl.handle.net/10919/34728.

MLA Handbook (7th Edition):

Shiraev, Dmitry Eric. “Inverse Reinforcement Learning and Routing Metric Discovery.” 2003. Web. 15 Dec 2019.

Vancouver:

Shiraev DE. Inverse Reinforcement Learning and Routing Metric Discovery. [Internet] [Masters thesis]. Virginia Tech; 2003. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10919/34728.

Council of Science Editors:

Shiraev DE. Inverse Reinforcement Learning and Routing Metric Discovery. [Masters Thesis]. Virginia Tech; 2003. Available from: http://hdl.handle.net/10919/34728


Wright State University

20. Nalamothu, Abhishek. Abusive and Hate Speech Tweets Detection with Text Generation.

Degree: MS, Computer Science, 2019, Wright State University

 According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online… (more)

Subjects/Keywords: Computer Science; Text generation; Generative adversarial network; Inverse Reinforcement Learning; Online Harassment detection

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APA (6th Edition):

Nalamothu, A. (2019). Abusive and Hate Speech Tweets Detection with Text Generation. (Masters Thesis). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305

Chicago Manual of Style (16th Edition):

Nalamothu, Abhishek. “Abusive and Hate Speech Tweets Detection with Text Generation.” 2019. Masters Thesis, Wright State University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305.

MLA Handbook (7th Edition):

Nalamothu, Abhishek. “Abusive and Hate Speech Tweets Detection with Text Generation.” 2019. Web. 15 Dec 2019.

Vancouver:

Nalamothu A. Abusive and Hate Speech Tweets Detection with Text Generation. [Internet] [Masters thesis]. Wright State University; 2019. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305.

Council of Science Editors:

Nalamothu A. Abusive and Hate Speech Tweets Detection with Text Generation. [Masters Thesis]. Wright State University; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305


University of Pennsylvania

21. Wen, Min. Reinforcement Learning With High-Level Task Specifications.

Degree: 2019, University of Pennsylvania

Reinforcement learning (RL) has been widely used, for example, in robotics, recommendation systems, and financial services. Existing RL algorithms typically optimize reward-based surrogates rather than… (more)

Subjects/Keywords: Game theory; Inverse reinforcement learning; Learning-based control; Learning from demonstration; Reinforcement learning; Temporal logic specifications; Artificial Intelligence and Robotics; Computer Sciences

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

APA (6th Edition):

Wen, M. (2019). Reinforcement Learning With High-Level Task Specifications. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/3509

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):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Thesis, University of Pennsylvania. Accessed December 15, 2019. https://repository.upenn.edu/edissertations/3509.

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

MLA Handbook (7th Edition):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Web. 15 Dec 2019.

Vancouver:

Wen M. Reinforcement Learning With High-Level Task Specifications. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2019 Dec 15]. Available from: https://repository.upenn.edu/edissertations/3509.

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

Council of Science Editors:

Wen M. Reinforcement Learning With High-Level Task Specifications. [Thesis]. University of Pennsylvania; 2019. Available from: https://repository.upenn.edu/edissertations/3509

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


Oregon State University

22. Proper, Scott. Scaling multiagent reinforcement learning.

Degree: PhD, Computer Science, 2009, Oregon State University

Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent… (more)

Subjects/Keywords: Reinforcement learning; Reinforcement learning

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APA (6th Edition):

Proper, S. (2009). Scaling multiagent reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/13662

Chicago Manual of Style (16th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed December 15, 2019. http://hdl.handle.net/1957/13662.

MLA Handbook (7th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 15 Dec 2019.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1957/13662.

Council of Science Editors:

Proper S. Scaling multiagent reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13662


Oregon State University

23. 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 December 15, 2019. http://hdl.handle.net/1957/25199.

MLA Handbook (7th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 15 Dec 2019.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Dec 15]. 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

24. Zhang, Ruohan. Action selection in modular reinforcement learning.

Degree: MSin Computer Sciences, Computer Sciences, 2014, University of Texas – Austin

Modular reinforcement learning is an approach to resolve the curse of dimensionality problem in traditional reinforcement learning. We design and implement a modular reinforcement learning(more)

Subjects/Keywords: Modular reinforcement learning; Action selection; Module weight

…in a RL problem with large state space. We propose to take a modular reinforcement learning… …introduces a test domain, and demonstrates our modular reinforcement learning algorithm. In Chapter… …Modular reinforcement learning [7, 10, 12, 20] decomposes original RL problem into… …results suggest modular reinforcement learning might be a promising approach to curse of… …dimensionality problem. A close relative to modular reinforcement learning is hierarchical… 

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APA (6th Edition):

Zhang, R. (2014). Action selection in modular reinforcement learning. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/25916

Chicago Manual of Style (16th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Masters Thesis, University of Texas – Austin. Accessed December 15, 2019. http://hdl.handle.net/2152/25916.

MLA Handbook (7th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Web. 15 Dec 2019.

Vancouver:

Zhang R. Action selection in modular reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2152/25916.

Council of Science Editors:

Zhang R. Action selection in modular reinforcement learning. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/25916


University of Illinois – Chicago

25. Monfort, Mathew. Methods in Large Scale Inverse Optimal Control.

Degree: 2016, University of Illinois – Chicago

 As our technology continues to evolve, so does the complexity of the problems that we expect our systems to solve. The challenge is that these… (more)

Subjects/Keywords: machine learning; artificial intelligence; inverse optimal control; graph search; autonomous agents; reinforcement learning; path distributions; robotic control; robotics; robots; activity recognition

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APA (6th Edition):

Monfort, M. (2016). Methods in Large Scale Inverse Optimal Control. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21540

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):

Monfort, Mathew. “Methods in Large Scale Inverse Optimal Control.” 2016. Thesis, University of Illinois – Chicago. Accessed December 15, 2019. http://hdl.handle.net/10027/21540.

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

MLA Handbook (7th Edition):

Monfort, Mathew. “Methods in Large Scale Inverse Optimal Control.” 2016. Web. 15 Dec 2019.

Vancouver:

Monfort M. Methods in Large Scale Inverse Optimal Control. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10027/21540.

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

Council of Science Editors:

Monfort M. Methods in Large Scale Inverse Optimal Control. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/21540

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

26. NGUYEN QUOC PHONG. AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING.

Degree: 2018, National University of Singapore

Subjects/Keywords: active learning; mutual information; inverse reinforcement learning

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

APA (6th Edition):

PHONG, N. Q. (2018). AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/150065

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):

PHONG, NGUYEN QUOC. “AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING.” 2018. Thesis, National University of Singapore. Accessed December 15, 2019. http://scholarbank.nus.edu.sg/handle/10635/150065.

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

MLA Handbook (7th Edition):

PHONG, NGUYEN QUOC. “AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING.” 2018. Web. 15 Dec 2019.

Vancouver:

PHONG NQ. AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING. [Internet] [Thesis]. National University of Singapore; 2018. [cited 2019 Dec 15]. Available from: http://scholarbank.nus.edu.sg/handle/10635/150065.

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

Council of Science Editors:

PHONG NQ. AN ALTERNATIVE INFORMATION-THEORETIC CRITERION FOR ACTIVE LEARNING. [Thesis]. National University of Singapore; 2018. Available from: http://scholarbank.nus.edu.sg/handle/10635/150065

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


Oregon State University

27. Lauer, Christopher Joseph. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.

Degree: PhD, 2017, Oregon State University

 Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Additionally,… (more)

Subjects/Keywords: reinforcement learning

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APA (6th Edition):

Lauer, C. J. (2017). Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61678

Chicago Manual of Style (16th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Doctoral Dissertation, Oregon State University. Accessed December 15, 2019. http://hdl.handle.net/1957/61678.

MLA Handbook (7th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Web. 15 Dec 2019.

Vancouver:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1957/61678.

Council of Science Editors:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61678


University of Illinois – Urbana-Champaign

28. Potok, Matthew. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

Reinforcement learning (RL) is a general method for agents to learn optimal control policies through exploration and experience. Due to its generality, RL can generate… (more)

Subjects/Keywords: Reinforcement Learning

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APA (6th Edition):

Potok, M. (2018). Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102518

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):

Potok, Matthew. “Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed December 15, 2019. http://hdl.handle.net/2142/102518.

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

MLA Handbook (7th Edition):

Potok, Matthew. “Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study.” 2018. Web. 15 Dec 2019.

Vancouver:

Potok M. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2142/102518.

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

Council of Science Editors:

Potok M. Safe reinforcement learning: An overview, a hybrid systems perspective, and a case study. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102518

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


Delft University of Technology

29. Van Diepen, M.D.M. Avoiding failure states during reinforcement learning:.

Degree: 2011, Delft University of Technology

 The Delft Biorobotics Laboratory develops bipedal humanoid robots. One of these robots, called LEO, is designed to learn to walk using reinforcement learning. During learning,… (more)

Subjects/Keywords: reinforcement learning

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APA (6th Edition):

Van Diepen, M. D. M. (2011). Avoiding failure states during reinforcement learning:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6

Chicago Manual of Style (16th Edition):

Van Diepen, M D M. “Avoiding failure states during reinforcement learning:.” 2011. Masters Thesis, Delft University of Technology. Accessed December 15, 2019. http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

MLA Handbook (7th Edition):

Van Diepen, M D M. “Avoiding failure states during reinforcement learning:.” 2011. Web. 15 Dec 2019.

Vancouver:

Van Diepen MDM. Avoiding failure states during reinforcement learning:. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2019 Dec 15]. Available from: http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

Council of Science Editors:

Van Diepen MDM. Avoiding failure states during reinforcement learning:. [Masters Thesis]. Delft University of Technology; 2011. Available from: http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6


Delft University of Technology

30. Van Rooijen, J.C. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.

Degree: 2012, Delft University of Technology

 The reinforcement learning (RL) framework enables to construct controllers that try to find find an optimal control strategy in an unknown environment by trial-and-error. After… (more)

Subjects/Keywords: reinforcement learning

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

APA (6th Edition):

Van Rooijen, J. C. (2012). Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38

Chicago Manual of Style (16th Edition):

Van Rooijen, J C. “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.” 2012. Masters Thesis, Delft University of Technology. Accessed December 15, 2019. http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

MLA Handbook (7th Edition):

Van Rooijen, J C. “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:.” 2012. Web. 15 Dec 2019.

Vancouver:

Van Rooijen JC. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2019 Dec 15]. Available from: http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

Council of Science Editors:

Van Rooijen JC. Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords:. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38

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