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You searched for subject:( en REINFORCEMENT LEARNING). Showing records 1 – 30 of 90376 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 September 25, 2020. http://hdl.handle.net/1957/25199.

MLA Handbook (7th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 25 Sep 2020.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2020 Sep 25]. 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


Oregon State University

2. 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 September 25, 2020. http://hdl.handle.net/1957/13662.

MLA Handbook (7th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 25 Sep 2020.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2020 Sep 25]. 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

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

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 September 25, 2020. 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. 25 Sep 2020.

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 2020 Sep 25]. 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

4. Frank, Mikhail Alexander. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.

Degree: 2014, Università della Svizzera italiana

 The next generation of intelligent robots will need to be able to plan reaches. Not just ballistic point to point reaches, but reaches around things… (more)

Subjects/Keywords: Reinforcement learning

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APA (6th Edition):

Frank, M. A. (2014). Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. (Thesis). Università della Svizzera italiana. Retrieved from http://doc.rero.ch/record/234387

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):

Frank, Mikhail Alexander. “Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.” 2014. Thesis, Università della Svizzera italiana. Accessed September 25, 2020. http://doc.rero.ch/record/234387.

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

MLA Handbook (7th Edition):

Frank, Mikhail Alexander. “Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning.” 2014. Web. 25 Sep 2020.

Vancouver:

Frank MA. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. [Internet] [Thesis]. Università della Svizzera italiana; 2014. [cited 2020 Sep 25]. Available from: http://doc.rero.ch/record/234387.

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

Council of Science Editors:

Frank MA. Learning to reach and reaching to learn: a unified approach to path planning and reactive control through reinforcement learning. [Thesis]. Università della Svizzera italiana; 2014. Available from: http://doc.rero.ch/record/234387

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


Delft University of Technology

5. Tian, Yuan (author). Model Free Reinforcement Learning with Stability Guarantee.

Degree: 2019, Delft University of Technology

Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics… (more)

Subjects/Keywords: Reinforcement Learning

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APA (6th Edition):

Tian, Y. (. (2019). Model Free Reinforcement Learning with Stability Guarantee. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c

Chicago Manual of Style (16th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

MLA Handbook (7th Edition):

Tian, Yuan (author). “Model Free Reinforcement Learning with Stability Guarantee.” 2019. Web. 25 Sep 2020.

Vancouver:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Sep 25]. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c.

Council of Science Editors:

Tian Y(. Model Free Reinforcement Learning with Stability Guarantee. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:dde4e58f-e109-4e7f-8ecb-ed1734294e5c


Delft University of Technology

6. Van Rooijen, J.C. (author). 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 (author). “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords.” 2012. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:94b81bc2-aff6-457f-9b54-be5e005def38.

MLA Handbook (7th Edition):

Van Rooijen, J C (author). “Learning Parameter Selection in Continuous Reinforcement Learning: Attempting to Reduce Tuning Effords.” 2012. Web. 25 Sep 2020.

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 2020 Sep 25]. 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


Delft University of Technology

7. Van Diepen, M.D.M. (author). 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 (author). “Avoiding failure states during reinforcement learning.” 2011. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:1f03c580-9fd5-4807-87b5-d70890e05ff6.

MLA Handbook (7th Edition):

Van Diepen, M D M (author). “Avoiding failure states during reinforcement learning.” 2011. Web. 25 Sep 2020.

Vancouver:

Van Diepen MDM(. Avoiding failure states during reinforcement learning. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2020 Sep 25]. 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


University of Illinois – Urbana-Champaign

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

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 September 25, 2020. 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. 25 Sep 2020.

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 2020 Sep 25]. 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


Pontifical Catholic University of Rio de Janeiro

9. EUGENIO PACELLI FERREIRA DIAS JUNIOR. [en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM.

Degree: 2012, Pontifical Catholic University of Rio de Janeiro

[pt] No ambiente da Internet, as informações que desejamos frequentemente encontram-se em diferentes localidades. Algumas aplicações, para funcionarem corretamente, precisam manter cópias locais de parte… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] APRENDIZADO DE MAQUINA; [en] MACHINE LEARNING; [pt] ALGORITMO; [en] ALGORITHM

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APA (6th Edition):

JUNIOR, E. P. F. D. (2012). [en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19637

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):

JUNIOR, EUGENIO PACELLI FERREIRA DIAS. “[en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM.” 2012. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19637.

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

MLA Handbook (7th Edition):

JUNIOR, EUGENIO PACELLI FERREIRA DIAS. “[en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM.” 2012. Web. 25 Sep 2020.

Vancouver:

JUNIOR EPFD. [en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2012. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19637.

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

Council of Science Editors:

JUNIOR EPFD. [en] USING REINFORCEMENT LEARNING ON WEB PAGES REVISITING PROBLEM. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2012. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19637

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


Pontifical Catholic University of Rio de Janeiro

10. LEONARDO ALFREDO FORERO MENDOZA. [en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING.

Degree: 2018, Pontifical Catholic University of Rio de Janeiro

[pt] Esta tese consiste na investigação e no desenvolvimento de estratégias de coordenação inteligente que possam ser integradas a modelos neuro-fuzzy hierárquicos para sistemas de… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] NEURO-FUZZY; [en] NEURO-FUZZY; [pt] COORDENACAO MULTIAGENTE; [en] MULTIAGENT COORDINATION

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APA (6th Edition):

MENDOZA, L. A. F. (2018). [en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35557

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):

MENDOZA, LEONARDO ALFREDO FORERO. “[en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING.” 2018. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35557.

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

MLA Handbook (7th Edition):

MENDOZA, LEONARDO ALFREDO FORERO. “[en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING.” 2018. Web. 25 Sep 2020.

Vancouver:

MENDOZA LAF. [en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35557.

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

Council of Science Editors:

MENDOZA LAF. [en] INTELLIGENT COORDINATION FOR MULTIAGENT BASED MODELS HIERARCHICAL NEURO-FUZZY WITH REINFORCEMENT LEARNING. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35557

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


Pontifical Catholic University of Rio de Janeiro

11. ADRIANO BRITO PEREIRA. [en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES.

Degree: 2017, Pontifical Catholic University of Rio de Janeiro

[pt] Este documento apresenta um novo algoritmo de aprendizado por reforço, o Q-Learning Pessimista. Nossa motivação é resolver o problema de gerar bots capazes de… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] APRENDIZADO DE MAQUINA; [en] MACHINE LEARNING; [pt] INTELIGENCIA ARTIFICIAL; [en] ARTIFICIAL INTELLIGENCE; [pt] JOGO; [en] GAME; [pt] Q-LEARNING PESSIMISTA; [pt] BOTS

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APA (6th Edition):

PEREIRA, A. B. (2017). [en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28809

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, ADRIANO BRITO. “[en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES.” 2017. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28809.

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

MLA Handbook (7th Edition):

PEREIRA, ADRIANO BRITO. “[en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES.” 2017. Web. 25 Sep 2020.

Vancouver:

PEREIRA AB. [en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2017. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28809.

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

Council of Science Editors:

PEREIRA AB. [en] PESSIMISTIC Q-LEARNING: AN ALGORITHM TO CREATE BOTS FOR TURN-BASED GAMES. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2017. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28809

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


University of New South Wales

12. Ismail, Hafsa. A neural network framework for combining different task types and motivations in motivated reinforcement learning.

Degree: Engineering & Information Technology, 2014, University of New South Wales

 Combining different motivation models for different task types within artificial agents has the potential to produce agents capable of a greater range of behaviours in… (more)

Subjects/Keywords: Motivated Reinforcement Learning

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APA (6th Edition):

Ismail, H. (2014). A neural network framework for combining different task types and motivations in motivated reinforcement learning. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Ismail, Hafsa. “A neural network framework for combining different task types and motivations in motivated reinforcement learning.” 2014. Masters Thesis, University of New South Wales. Accessed September 25, 2020. http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true.

MLA Handbook (7th Edition):

Ismail, Hafsa. “A neural network framework for combining different task types and motivations in motivated reinforcement learning.” 2014. Web. 25 Sep 2020.

Vancouver:

Ismail H. A neural network framework for combining different task types and motivations in motivated reinforcement learning. [Internet] [Masters thesis]. University of New South Wales; 2014. [cited 2020 Sep 25]. Available from: http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true.

Council of Science Editors:

Ismail H. A neural network framework for combining different task types and motivations in motivated reinforcement learning. [Masters Thesis]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53975 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12686/SOURCE02?view=true


Delft University of Technology

13. Keulen, Bart (author). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.

Degree: 2018, Delft University of Technology

 An important problem in reinforcement learning is the exploration-exploitation dilemma. Especially for environments with sparse or misleading rewards it has proven difficult to construct a… (more)

Subjects/Keywords: Reinforcement Learning; Exploration

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APA (6th Edition):

Keulen, B. (. (2018). Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422

Chicago Manual of Style (16th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

MLA Handbook (7th Edition):

Keulen, Bart (author). “Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning.” 2018. Web. 25 Sep 2020.

Vancouver:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Sep 25]. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422.

Council of Science Editors:

Keulen B(. Smart Start: A Directed and Persistent Exploration Framework for Reinforcement Learning. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:eca20454-7905-42a1-9fb0-f72776fd5422


Pontifical Catholic University of Rio de Janeiro

14. DIEGO CEDRIM GOMES REGO. [en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY.

Degree: 2009, Pontifical Catholic University of Rio de Janeiro

[pt] Apresentamos um estudo empírico comparativo para o problema de Execução Otimizada de Transações nos mercados financeiros modernos. Construímos um simulador dos mercados financeiros, e… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] APRENDIZADO DE MAQUINA; [en] MACHINE LEARNING; [pt] OTIMIZACAO; [en] OPTIMIZATION; [pt] MERCADOS FINANCEIROS; [en] FINANCIAL MARKETS

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

APA (6th Edition):

REGO, D. C. G. (2009). [en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13222

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):

REGO, DIEGO CEDRIM GOMES. “[en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY.” 2009. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13222.

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

MLA Handbook (7th Edition):

REGO, DIEGO CEDRIM GOMES. “[en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY.” 2009. Web. 25 Sep 2020.

Vancouver:

REGO DCG. [en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2009. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13222.

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

Council of Science Editors:

REGO DCG. [en] OPTIMIZED FINANCIAL TRADE EXECUTION A EMPIRICAL STUDY. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2009. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13222

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


Pontifical Catholic University of Rio de Janeiro

15. FABIO JESSEN WERNECK DE ALMEIDA MARTINS. [en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL.

Degree: 2010, Pontifical Catholic University of Rio de Janeiro

[pt] Neste trabalho foram desenvolvidos e avaliados métodos com o objetivo de melhorar e acelerar o processo de aprendizado do modelo de Reinforcement Learning Neuro-Fuzzy… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] AGENTE INTELIGENTE; [en] INTELLIGENT AGENT; [pt] APRENDIZADO AUTOMATICO; [en] AUTOMATIC LEARNING; [pt] NEURO-FUZZY; [en] NEURO-FUZZY

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

APA (6th Edition):

MARTINS, F. J. W. D. A. (2010). [en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16421

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):

MARTINS, FABIO JESSEN WERNECK DE ALMEIDA. “[en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL.” 2010. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16421.

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

MLA Handbook (7th Edition):

MARTINS, FABIO JESSEN WERNECK DE ALMEIDA. “[en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL.” 2010. Web. 25 Sep 2020.

Vancouver:

MARTINS FJWDA. [en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2010. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16421.

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

Council of Science Editors:

MARTINS FJWDA. [en] METHODS FOR ACCELERATION OF LEARNING PROCESS OF REINFORCEMENT LEARNING NEURO-FUZZY HIERARCHICAL POLITREE MODEL. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2010. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16421

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


Oregon State University

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

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

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

Subjects/Keywords: Machine Learning; Reinforcement learning

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Doctoral Dissertation, Oregon State University. Accessed September 25, 2020. http://hdl.handle.net/1957/34550.

MLA Handbook (7th Edition):

Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Web. 25 Sep 2020.

Vancouver:

Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/1957/34550.

Council of Science Editors:

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


University of Aberdeen

17. Alexander, John W. Transfer in reinforcement learning.

Degree: PhD, 2015, University of Aberdeen

 The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior… (more)

Subjects/Keywords: 004; Reinforcement learning; Learning

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

APA (6th Edition):

Alexander, J. W. (2015). Transfer in reinforcement learning. (Doctoral Dissertation). University of Aberdeen. Retrieved from http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561

Chicago Manual of Style (16th Edition):

Alexander, John W. “Transfer in reinforcement learning.” 2015. Doctoral Dissertation, University of Aberdeen. Accessed September 25, 2020. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561.

MLA Handbook (7th Edition):

Alexander, John W. “Transfer in reinforcement learning.” 2015. Web. 25 Sep 2020.

Vancouver:

Alexander JW. Transfer in reinforcement learning. [Internet] [Doctoral dissertation]. University of Aberdeen; 2015. [cited 2020 Sep 25]. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561.

Council of Science Editors:

Alexander JW. Transfer in reinforcement learning. [Doctoral Dissertation]. University of Aberdeen; 2015. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.675561


Rutgers University

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

Degree: PhD, Computer Science, 2015, Rutgers University

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

Subjects/Keywords: Reinforcement learning; Machine learning; Algorithms

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 25, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

MLA Handbook (7th Edition):

Marivate, Vukosi N. “Improved empirical methods in reinforcement-learning evaluation.” 2015. Web. 25 Sep 2020.

Vancouver:

Marivate VN. Improved empirical methods in reinforcement-learning evaluation. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 25]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46389/.

Council of Science Editors:

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

19. Perundurai Rajasekaran, Siddharthan. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.

Degree: MS, 2017, Worcester Polytechnic Institute

 "This thesis focuses on two key problems in reinforcement learning: How to design reward functions to obtain intended behaviors in autonomous systems using the learning-based… (more)

Subjects/Keywords: Learning with uncertainty; Unsupervised learning; Reinforcement Learning; Inverse Reinforcement Learning

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APA (6th Edition):

Perundurai Rajasekaran, S. (2017). Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205

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):

Perundurai Rajasekaran, Siddharthan. “Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.” 2017. Thesis, Worcester Polytechnic Institute. Accessed September 25, 2020. etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205.

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

MLA Handbook (7th Edition):

Perundurai Rajasekaran, Siddharthan. “Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.” 2017. Web. 25 Sep 2020.

Vancouver:

Perundurai Rajasekaran S. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. [Internet] [Thesis]. Worcester Polytechnic Institute; 2017. [cited 2020 Sep 25]. Available from: etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205.

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

Council of Science Editors:

Perundurai Rajasekaran S. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. [Thesis]. Worcester Polytechnic Institute; 2017. Available from: etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205

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


Pontifical Catholic University of Rio de Janeiro

20. MARCELO FRANCA CORREA. [en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS.

Degree: 2013, Pontifical Catholic University of Rio de Janeiro

 [pt] Os benefícios trazidos pela aplicação de Sistemas Multi-Agentes (SMA) são diversos. Através da computação paralela, agentes podem trabalhar em conjunto para explorar melhor a… (more)

Subjects/Keywords: [pt] APRENDIZADO POR REFORCO; [en] REINFORCEMENT LEARNING; [pt] SISTEMAS MULTI-AGENTES; [en] MULTI-AGENT SYSTEMS; [pt] AGENTE INTELIGENTE; [en] INTELLIGENT AGENT; [pt] NEURO-FUZZY; [en] NEURO-FUZZY

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APA (6th Edition):

CORREA, M. F. (2013). [en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21194

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):

CORREA, MARCELO FRANCA. “[en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS.” 2013. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed September 25, 2020. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21194.

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

MLA Handbook (7th Edition):

CORREA, MARCELO FRANCA. “[en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS.” 2013. Web. 25 Sep 2020.

Vancouver:

CORREA MF. [en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2013. [cited 2020 Sep 25]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21194.

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

Council of Science Editors:

CORREA MF. [en] HIERARCHICAL NEURAL FUZZY MODELS BASED ON REINFORCEMENT LEARNING OF INTELLIGENT AGENTS. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2013. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=21194

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


University of Waterloo

21. Gaurav, Ashish. Safety-Oriented Stability Biases for Continual Learning.

Degree: 2020, University of Waterloo

 Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that… (more)

Subjects/Keywords: deep learning; continual learning; classification; reinforcement learning

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

APA (6th Edition):

Gaurav, A. (2020). Safety-Oriented Stability Biases for Continual Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15579

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):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Thesis, University of Waterloo. Accessed September 25, 2020. http://hdl.handle.net/10012/15579.

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

MLA Handbook (7th Edition):

Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Web. 25 Sep 2020.

Vancouver:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10012/15579.

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

Council of Science Editors:

Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15579

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


Hong Kong University of Science and Technology

22. Wellmer, Zachary William CSE. Building and leveraging implicit models for policy gradient methods.

Degree: 2019, Hong Kong University of Science and Technology

 In this thesis, we study Policy Prediction Network and Policy Tree Network, both are deep reinforcement learning architectures offering ways to improve sample complexity and… (more)

Subjects/Keywords: Reinforcement learning ; Machine learning ; Implicit learning

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APA (6th Edition):

Wellmer, Z. W. C. (2019). Building and leveraging implicit models for policy gradient methods. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html

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

Chicago Manual of Style (16th Edition):

Wellmer, Zachary William CSE. “Building and leveraging implicit models for policy gradient methods.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed September 25, 2020. http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Wellmer, Zachary William CSE. “Building and leveraging implicit models for policy gradient methods.” 2019. Web. 25 Sep 2020.

Vancouver:

Wellmer ZWC. Building and leveraging implicit models for policy gradient methods. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2020 Sep 25]. Available from: http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html.

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

Council of Science Editors:

Wellmer ZWC. Building and leveraging implicit models for policy gradient methods. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html

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


University of Texas – Austin

23. Shah, Rishi Alpesh. Deep R learning for continual area sweeping.

Degree: MSin Computational Science, Engineering, and Mathematics, Computational Science, Engineering, and Mathematics, 2019, University of Texas – Austin

 In order to maintain robustness, autonomous robots need to constantly update their knowledge of the environment, which can be expensive when they are deployed in… (more)

Subjects/Keywords: Machine learning; Reinforcement learning; Robotics; Robot learning

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

APA (6th Edition):

Shah, R. A. (2019). Deep R learning for continual area sweeping. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/8201

Chicago Manual of Style (16th Edition):

Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Masters Thesis, University of Texas – Austin. Accessed September 25, 2020. http://dx.doi.org/10.26153/tsw/8201.

MLA Handbook (7th Edition):

Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Web. 25 Sep 2020.

Vancouver:

Shah RA. Deep R learning for continual area sweeping. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2020 Sep 25]. Available from: http://dx.doi.org/10.26153/tsw/8201.

Council of Science Editors:

Shah RA. Deep R learning for continual area sweeping. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/8201


Universitat de Girona

24. El-Fakdi Sencianes, Andrés. Gradient-based reinforcement learning techniques for underwater robotics behavior learning.

Degree: Departament d'Arquitectura i Tecnologia de Computadors, 2011, Universitat de Girona

 A considerable interest has arisen around Autonomous Underwater Vehicle (AUV) applications. AUVs are very useful because of their size and their independence from human operators.… (more)

Subjects/Keywords: Reinforcement learning; Underwater robotics; Learning in robotics; Aprendizaje por refuerzo; Robótica submarina; Aprendizaje en robótica; Aprenentatge per reforç; Robòtica submarina; Aprenentatge en robòtica; 621.3; 68

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

APA (6th Edition):

El-Fakdi Sencianes, A. (2011). Gradient-based reinforcement learning techniques for underwater robotics behavior learning. (Thesis). Universitat de Girona. Retrieved from http://hdl.handle.net/10803/7610

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):

El-Fakdi Sencianes, Andrés. “Gradient-based reinforcement learning techniques for underwater robotics behavior learning.” 2011. Thesis, Universitat de Girona. Accessed September 25, 2020. http://hdl.handle.net/10803/7610.

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

MLA Handbook (7th Edition):

El-Fakdi Sencianes, Andrés. “Gradient-based reinforcement learning techniques for underwater robotics behavior learning.” 2011. Web. 25 Sep 2020.

Vancouver:

El-Fakdi Sencianes A. Gradient-based reinforcement learning techniques for underwater robotics behavior learning. [Internet] [Thesis]. Universitat de Girona; 2011. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10803/7610.

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

Council of Science Editors:

El-Fakdi Sencianes A. Gradient-based reinforcement learning techniques for underwater robotics behavior learning. [Thesis]. Universitat de Girona; 2011. Available from: http://hdl.handle.net/10803/7610

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


Delft University of Technology

25. Kovács, B. (author). Safe reinforcement learning in long-horizon partially observable environments.

Degree: 2020, Delft University of Technology

 Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like… (more)

Subjects/Keywords: reinforcement learning; self-attention; partially observable; deep reinforcement learning; safe reinforcement learning

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

APA (6th Edition):

Kovács, B. (. (2020). Safe reinforcement learning in long-horizon partially observable environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0

Chicago Manual of Style (16th Edition):

Kovács, B (author). “Safe reinforcement learning in long-horizon partially observable environments.” 2020. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0.

MLA Handbook (7th Edition):

Kovács, B (author). “Safe reinforcement learning in long-horizon partially observable environments.” 2020. Web. 25 Sep 2020.

Vancouver:

Kovács B(. Safe reinforcement learning in long-horizon partially observable environments. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Sep 25]. Available from: http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0.

Council of Science Editors:

Kovács B(. Safe reinforcement learning in long-horizon partially observable environments. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:83556b37-1484-41f4-b168-3bd316def4a0


University of Waterloo

26. Bhalla, Sushrut. Deep Multi Agent Reinforcement Learning for Autonomous Driving.

Degree: 2020, University of Waterloo

 Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative Multi-Agent Deep Reinforcement Learning(more)

Subjects/Keywords: Machine Learning; Reinforcement Learning; Multi-Agent Reinforcement Learning

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

APA (6th Edition):

Bhalla, S. (2020). Deep Multi Agent Reinforcement Learning for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15799

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):

Bhalla, Sushrut. “Deep Multi Agent Reinforcement Learning for Autonomous Driving.” 2020. Thesis, University of Waterloo. Accessed September 25, 2020. http://hdl.handle.net/10012/15799.

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

MLA Handbook (7th Edition):

Bhalla, Sushrut. “Deep Multi Agent Reinforcement Learning for Autonomous Driving.” 2020. Web. 25 Sep 2020.

Vancouver:

Bhalla S. Deep Multi Agent Reinforcement Learning for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Sep 25]. Available from: http://hdl.handle.net/10012/15799.

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

Council of Science Editors:

Bhalla S. Deep Multi Agent Reinforcement Learning for Autonomous Driving. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15799

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


NSYSU

27. Lin, Kun-da. Deep Reinforcement Learning with a Gating Network.

Degree: Master, Electrical Engineering, 2017, NSYSU

Reinforcement Learning (RL) is a good way to train the robot since it doesn't need an exact model of the environment. All need is to… (more)

Subjects/Keywords: Reinforcement Learning; Deep Reinforcement Learning; Deep Learning; Gating network; Neural network

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

APA (6th Edition):

Lin, K. (2017). Deep Reinforcement Learning with a Gating Network. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

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, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Thesis, NSYSU. Accessed September 25, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

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

MLA Handbook (7th Edition):

Lin, Kun-da. “Deep Reinforcement Learning with a Gating Network.” 2017. Web. 25 Sep 2020.

Vancouver:

Lin K. Deep Reinforcement Learning with a Gating Network. [Internet] [Thesis]. NSYSU; 2017. [cited 2020 Sep 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536.

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

Council of Science Editors:

Lin K. Deep Reinforcement Learning with a Gating Network. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0223117-131536

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


NSYSU

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

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 September 25, 2020. 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. 25 Sep 2020.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Sep 25]. 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

29. 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 September 25, 2020. 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. 25 Sep 2020.

Vancouver:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Sep 25]. 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


Delft University of Technology

30. van der Wijden, R. (author). Preference-driven demonstrations ranking for inverse reinforcement learning.

Degree: 2016, Delft University of Technology

New flexible teaching methods for robotics are needed to automate repetitive tasks that are currently still done by humans. For limited batch sizes, it is… (more)

Subjects/Keywords: robotics; reinforcement learning; preference learning; inverse reinforcement learning

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

APA (6th Edition):

van der Wijden, R. (. (2016). Preference-driven demonstrations ranking for inverse reinforcement learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98

Chicago Manual of Style (16th Edition):

van der Wijden, R (author). “Preference-driven demonstrations ranking for inverse reinforcement learning.” 2016. Masters Thesis, Delft University of Technology. Accessed September 25, 2020. http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98.

MLA Handbook (7th Edition):

van der Wijden, R (author). “Preference-driven demonstrations ranking for inverse reinforcement learning.” 2016. Web. 25 Sep 2020.

Vancouver:

van der Wijden R(. Preference-driven demonstrations ranking for inverse reinforcement learning. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Sep 25]. Available from: http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98.

Council of Science Editors:

van der Wijden R(. Preference-driven demonstrations ranking for inverse reinforcement learning. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98

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