subject:(Policy gradient)

University of Alberta

1. Dick, Travis B. Policy Gradient Reinforcement Learning Without Regret.

Degree: MS, Department of Computing Science, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/df65vb663

► This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of… (more)

▼ This thesis consists of two independent projects, each contributing to a central goal of artificial intelligence research: to build computer systems that are capable of performing tasks and solving problems without problem-specific direction from us, their designers. I focus on two formal learning problems that have a strong mathematical grounding. Many real-world learning problems can be cast as instances of one of these two problems. Whenever our translation from the real to the formal accurately captures the character of the problem, then the mathematical arguments we make about algorithms in the formal setting will approximately hold in the real-world as well. The first project focuses on an open question in the theory of policy gradient reinforcement learning methods. These methods learn by trial and error and decide whether a trial was good or bad by comparing its outcome to a given baseline. The baseline has no impact on the formal asymptotic guarantees for policy gradient methods, but it does alter their finite-time behaviour. This immediately raises the question: which baseline should we use? I propose that the baseline should be chosen such that a certain estimate used internally by policy gradient methods has the smallest error. I prove that, under slightly idealistic assumptions, this baseline gives a good upper bound on the regret of policy gradient methods. I derive closed-form expressions for this baseline in terms of properties of the formal learning problem and the computer's behaviour. The quantities appearing in the closed form expressions are often unknown, so I also propose two algorithms for estimating this baseline from only known quantities. Finally, I present an empirical comparison of commonly used baselines that demonstrates improved performance when using my proposed baseline. The second project focuses on a recently proposed class of formal learning problems that is in the intersection of two fields of computing science research: reinforcement learning and online learning. The considered problems are sometimes called online Markov decision processes, or Markov decision processes with changing rewards. The unique property of this class is that it assumes the computer's environment is adversarial, as though it were playing a game against the computer. This is in contrast to the more common assumption that the environment's behaviour is determined entirely by stochastic models. I propose three new algorithms for learning in Markov decision processes with changing rewards under various conditions. I prove theoretical performance guarantees for each algorithm that either complement or improve the best existing results and that often hold even under weaker assumptions. This comes at the cost of increased (but still polynomial) computational complexity. Finally, in the development and analysis of these algorithms, it was necessary to analyze an approximate version of a well-known optimization algorithm called online mirror ascent. To the best of my knowledge, this is the first rigorous analysis…

Subjects/Keywords: Policy Gradient; Baseline; Reinforcement Learning

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Dick, T. B. (2015). Policy Gradient Reinforcement Learning Without Regret. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/df65vb663

Chicago Manual of Style (16^th Edition):

Dick, Travis B. “Policy Gradient Reinforcement Learning Without Regret.” 2015. Masters Thesis, University of Alberta. Accessed January 24, 2021. https://era.library.ualberta.ca/files/df65vb663.

MLA Handbook (7^th Edition):

Dick, Travis B. “Policy Gradient Reinforcement Learning Without Regret.” 2015. Web. 24 Jan 2021.

Vancouver:

Dick TB. Policy Gradient Reinforcement Learning Without Regret. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Jan 24]. Available from: https://era.library.ualberta.ca/files/df65vb663.

Council of Science Editors:

Dick TB. Policy Gradient Reinforcement Learning Without Regret. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/df65vb663

Arizona State University

2. Barron, Trevor Paul. Adaptive Curvature for Stochastic Optimization.

Degree: Computer Science, 2019, Arizona State University

URL: http://repository.asu.edu/items/53675

► This thesis presents a family of adaptive curvature methods for gradient-based stochastic optimization. In particular, a general algorithmic framework is introduced along with a practical… (more)

Subjects/Keywords: Statistics; Robotics; Natural gradient descent; Policy gradient methods; Truncated Newton methods

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Barron, T. P. (2019). Adaptive Curvature for Stochastic Optimization. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/53675

Chicago Manual of Style (16^th Edition):

Barron, Trevor Paul. “Adaptive Curvature for Stochastic Optimization.” 2019. Masters Thesis, Arizona State University. Accessed January 24, 2021. http://repository.asu.edu/items/53675.

MLA Handbook (7^th Edition):

Barron, Trevor Paul. “Adaptive Curvature for Stochastic Optimization.” 2019. Web. 24 Jan 2021.

Vancouver:

Barron TP. Adaptive Curvature for Stochastic Optimization. [Internet] [Masters thesis]. Arizona State University; 2019. [cited 2021 Jan 24]. Available from: http://repository.asu.edu/items/53675.

Council of Science Editors:

Barron TP. Adaptive Curvature for Stochastic Optimization. [Masters Thesis]. Arizona State University; 2019. Available from: http://repository.asu.edu/items/53675

University of Alberta

3. Hackman, Leah M. Faster Gradient-TD Algorithms.

Degree: MS, Department of Computing Science, 2012, University of Alberta

URL: https://era.library.ualberta.ca/files/6w924d09v

► Gradient-TD methods are a new family of learning algorithms that are stable and convergent under a wider range of conditions than previous reinforcement learning algorithms.… (more)

Subjects/Keywords: Aritificial; Off-Policy; Gradient-TD; Intelligence; Reinforcement; Hybrid-GQ; Learning

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Hackman, L. M. (2012). Faster Gradient-TD Algorithms. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/6w924d09v

Chicago Manual of Style (16^th Edition):

Hackman, Leah M. “Faster Gradient-TD Algorithms.” 2012. Masters Thesis, University of Alberta. Accessed January 24, 2021. https://era.library.ualberta.ca/files/6w924d09v.

MLA Handbook (7^th Edition):

Hackman, Leah M. “Faster Gradient-TD Algorithms.” 2012. Web. 24 Jan 2021.

Vancouver:

Hackman LM. Faster Gradient-TD Algorithms. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2021 Jan 24]. Available from: https://era.library.ualberta.ca/files/6w924d09v.

Council of Science Editors:

Hackman LM. Faster Gradient-TD Algorithms. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/6w924d09v

University of Alberta

4. Das Gupta, Ujjwal. Adaptive Representation for Policy Gradient.

Degree: MS, Department of Computing Science, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/zk51vk289

► Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that… (more)

Subjects/Keywords: Representation Learning; Decision Trees; Policy Gradient; Reinforcement Learning

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Das Gupta, U. (2015). Adaptive Representation for Policy Gradient. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zk51vk289

Chicago Manual of Style (16^th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Masters Thesis, University of Alberta. Accessed January 24, 2021. https://era.library.ualberta.ca/files/zk51vk289.

MLA Handbook (7^th Edition):

Das Gupta, Ujjwal. “Adaptive Representation for Policy Gradient.” 2015. Web. 24 Jan 2021.

Vancouver:

Das Gupta U. Adaptive Representation for Policy Gradient. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Jan 24]. Available from: https://era.library.ualberta.ca/files/zk51vk289.

Council of Science Editors:

Das Gupta U. Adaptive Representation for Policy Gradient. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/zk51vk289

Delft University of Technology

5. Goedhart, Menno (author). Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:0f103559-d985-47b7-b145-fc814527f307

► Flight control of the DelFly is challenging, because of its complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to… (more)

Subjects/Keywords: Reinforcement Learning; DelFly; Flapping Wing; Classification; Machine Learning; Policy Gradient

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Goedhart, M. (. (2017). Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0f103559-d985-47b7-b145-fc814527f307

Chicago Manual of Style (16^th Edition):

Goedhart, Menno (author). “Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly.” 2017. Masters Thesis, Delft University of Technology. Accessed January 24, 2021. http://resolver.tudelft.nl/uuid:0f103559-d985-47b7-b145-fc814527f307.

MLA Handbook (7^th Edition):

Goedhart, Menno (author). “Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly.” 2017. Web. 24 Jan 2021.

Vancouver:

Goedhart M(. Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 24]. Available from: http://resolver.tudelft.nl/uuid:0f103559-d985-47b7-b145-fc814527f307.

Council of Science Editors:

Goedhart M(. Intelligent Flapping Wing Control: Reinforcement Learning for the DelFly. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:0f103559-d985-47b7-b145-fc814527f307

6. Poulin, Nolan. Proactive Planning through Active Policy Inference in Stochastic Environments.

Degree: MS, 2018, Worcester Polytechnic Institute

URL: etd-052818-100711 ; https://digitalcommons.wpi.edu/etd-theses/1267

► In multi-agent Markov Decision Processes, a controllable agent must perform optimal planning in a dynamic and uncertain environment that includes another unknown and uncontrollable… (more)

Subjects/Keywords: active learning markov decision process softmax boltzmann policy gradient

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Poulin, N. (2018). Proactive Planning through Active Policy Inference in Stochastic Environments. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-052818-100711 ; https://digitalcommons.wpi.edu/etd-theses/1267

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

Chicago Manual of Style (16^th Edition):

Poulin, Nolan. “Proactive Planning through Active Policy Inference in Stochastic Environments.” 2018. Thesis, Worcester Polytechnic Institute. Accessed January 24, 2021. etd-052818-100711 ; https://digitalcommons.wpi.edu/etd-theses/1267.

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

MLA Handbook (7^th Edition):

Poulin, Nolan. “Proactive Planning through Active Policy Inference in Stochastic Environments.” 2018. Web. 24 Jan 2021.

Vancouver:

Poulin N. Proactive Planning through Active Policy Inference in Stochastic Environments. [Internet] [Thesis]. Worcester Polytechnic Institute; 2018. [cited 2021 Jan 24]. Available from: etd-052818-100711 ; https://digitalcommons.wpi.edu/etd-theses/1267.

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

Council of Science Editors:

Poulin N. Proactive Planning through Active Policy Inference in Stochastic Environments. [Thesis]. Worcester Polytechnic Institute; 2018. Available from: etd-052818-100711 ; https://digitalcommons.wpi.edu/etd-theses/1267

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

7. Könönen, Ville. Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches.

Degree: 2004, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2004/isbn9512273594/

►

Modern computing systems are distributed, large, and heterogeneous. Computers, other information processing devices and humans are very tightly connected with each other and therefore it… (more)

▼

Modern computing systems are distributed, large, and heterogeneous. Computers, other information processing devices and humans are very tightly connected with each other and therefore it would be preferable to handle these entities more as agents than stand-alone systems. One of the goals of artificial intelligence is to understand interactions between entities, whether they are artificial or natural, and to suggest how to make good decisions while taking other decision makers into account. In this thesis, these interactions between intelligent and rational agents are modeled with Markov games and the emphasis is on adaptation and learning in multiagent systems. Markov games are a general mathematical tool for modeling interactions between multiple agents. The model is very general, for example common board games are special instances of Markov games, and particularly interesting because it forms an intersection of two distinct research disciplines: machine learning and game theory. Markov games extend Markov decision processes, a well-known tool for modeling single-agent problems, to multiagent domains. On the other hand, Markov games can be seen as a dynamic extension to strategic form games, which are standard models in traditional game theory. From the computer science perspective, Markov games provide a flexible and efficient way to describe different social interactions between intelligent agents. This thesis studies different aspects of learning in Markov games. From the machine learning perspective, the focus is on a very general learning model, i.e. reinforcement learning, in which the goal is to maximize the long-time performance of the learning agent. The thesis introduces an asymmetric learning model that is computationally efficient in multiagent systems and enables the construction of different agent hierarchies. In multiagent reinforcement learning systems based on Markov games, the space and computational requirements grow very quickly with the number of learning agents and the size of the problem instance. Therefore, it is necessary to use function approximators, such as neural networks, to model agents in many real-world applications. In this thesis, various numeric learning methods are proposed for multiagent learning problems. The proposed methods are tested with small but non-trivial example problems from different research areas including artificial robot navigation, simplified soccer game, and automated pricing models for intelligent agents. The thesis also contains an extensive literature survey on multiagent reinforcement learning and various methods based on Markov games. Additionally, game-theoretic methods and methods originated from computer science for multiagent learning and decision making are compared.

Dissertations in computer and information science. Report D, ISSN 1459-7020; 8

Advisors/Committee Members: Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science.

Subjects/Keywords: Markov games; reinforcement learning; Nash equilibrium; Stackelberg equilibrium; value function approximation; policy gradient

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Könönen, V. (2004). Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2004/isbn9512273594/

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

Chicago Manual of Style (16^th Edition):

Könönen, Ville. “Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches.” 2004. Thesis, Helsinki University of Technology. Accessed January 24, 2021. http://lib.tkk.fi/Diss/2004/isbn9512273594/.

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

MLA Handbook (7^th Edition):

Könönen, Ville. “Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches.” 2004. Web. 24 Jan 2021.

Vancouver:

Könönen V. Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches. [Internet] [Thesis]. Helsinki University of Technology; 2004. [cited 2021 Jan 24]. Available from: http://lib.tkk.fi/Diss/2004/isbn9512273594/.

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

Council of Science Editors:

Könönen V. Multiagent Reinforcement Learning in Markov Games: Asymmetric and Symmetric Approaches. [Thesis]. Helsinki University of Technology; 2004. Available from: http://lib.tkk.fi/Diss/2004/isbn9512273594/

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

University of Alberta

8. Maei, Hamid Reza. Gradient Temporal-Difference Learning Algorithms.

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

URL: https://era.library.ualberta.ca/files/8s45q967t

► We present a new family of gradient temporal-difference (TD) learning methods with function approximation whose complexity, both in terms of memory and per-time-step computation, scales… (more)

Subjects/Keywords: Policy Evaluation; Temporal-Difference learning; Reinforcement Learning; Stochastic Gradient-Descent; Value Function Approximation

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Maei, H. R. (2011). Gradient Temporal-Difference Learning Algorithms. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/8s45q967t

Chicago Manual of Style (16^th Edition):

Maei, Hamid Reza. “Gradient Temporal-Difference Learning Algorithms.” 2011. Doctoral Dissertation, University of Alberta. Accessed January 24, 2021. https://era.library.ualberta.ca/files/8s45q967t.

MLA Handbook (7^th Edition):

Maei, Hamid Reza. “Gradient Temporal-Difference Learning Algorithms.” 2011. Web. 24 Jan 2021.

Vancouver:

Maei HR. Gradient Temporal-Difference Learning Algorithms. [Internet] [Doctoral dissertation]. University of Alberta; 2011. [cited 2021 Jan 24]. Available from: https://era.library.ualberta.ca/files/8s45q967t.

Council of Science Editors:

Maei HR. Gradient Temporal-Difference Learning Algorithms. [Doctoral Dissertation]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/8s45q967t

University of Washington

9. Deole, Aditya. Model Free Optimal Control Approach for UAVs.

Degree: 2020, University of Washington

URL: http://hdl.handle.net/1773/46114

► This thesis discusses use of model free control algorithms for application on an UAV. The work contrasts use of LQR based methods to conventional model… (more)

Subjects/Keywords: LQR; Model-free; Policy gradient; Q-learning; UAV; Mechanical engineering; Mechanical engineering

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Deole, A. (2020). Model Free Optimal Control Approach for UAVs. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/46114

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

Chicago Manual of Style (16^th Edition):

Deole, Aditya. “Model Free Optimal Control Approach for UAVs.” 2020. Thesis, University of Washington. Accessed January 24, 2021. http://hdl.handle.net/1773/46114.

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

MLA Handbook (7^th Edition):

Deole, Aditya. “Model Free Optimal Control Approach for UAVs.” 2020. Web. 24 Jan 2021.

Vancouver:

Deole A. Model Free Optimal Control Approach for UAVs. [Internet] [Thesis]. University of Washington; 2020. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/1773/46114.

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

Council of Science Editors:

Deole A. Model Free Optimal Control Approach for UAVs. [Thesis]. University of Washington; 2020. Available from: http://hdl.handle.net/1773/46114

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

University of North Texas

10. Cox, Carissa. Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area.

Degree: 2011, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc84194/

► Land use regulations are typically established as a response to development activity. For effective growth management and habitat preservation, the opposite should occur. This study… (more)

Subjects/Keywords: Tree preservation; urban rural gradient; hot spot rendering; preservation policy; DFW; ordinances; internal evaluation

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Cox, C. (2011). Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc84194/

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

Chicago Manual of Style (16^th Edition):

Cox, Carissa. “Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area.” 2011. Thesis, University of North Texas. Accessed January 24, 2021. https://digital.library.unt.edu/ark:/67531/metadc84194/.

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

MLA Handbook (7^th Edition):

Cox, Carissa. “Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area.” 2011. Web. 24 Jan 2021.

Vancouver:

Cox C. Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area. [Internet] [Thesis]. University of North Texas; 2011. [cited 2021 Jan 24]. Available from: https://digital.library.unt.edu/ark:/67531/metadc84194/.

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

Council of Science Editors:

Cox C. Spatial Patterns in Development Regulation: Tree Preservation Ordinances of the DFW Metropolitan Area. [Thesis]. University of North Texas; 2011. Available from: https://digital.library.unt.edu/ark:/67531/metadc84194/

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

KTH

11. Olafsson, Björgvin. Partially Observable Markov Decision Processes for Faster Object Recognition.

Degree: Computer Science and Communication (CSC), 2016, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632

► Object recognition in the real world is a big challenge in the field of computer vision. Given the potentially enormous size of the search… (more)

Subjects/Keywords: pomdp; policy gradient; optimal control; object detection; computer vision; information rewards; fixation policy; observation model; Computer Sciences; Datavetenskap (datalogi)

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Olafsson, B. (2016). Partially Observable Markov Decision Processes for Faster Object Recognition. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632

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

Chicago Manual of Style (16^th Edition):

Olafsson, Björgvin. “Partially Observable Markov Decision Processes for Faster Object Recognition.” 2016. Thesis, KTH. Accessed January 24, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632.

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

MLA Handbook (7^th Edition):

Olafsson, Björgvin. “Partially Observable Markov Decision Processes for Faster Object Recognition.” 2016. Web. 24 Jan 2021.

Vancouver:

Olafsson B. Partially Observable Markov Decision Processes for Faster Object Recognition. [Internet] [Thesis]. KTH; 2016. [cited 2021 Jan 24]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632.

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

Council of Science Editors:

Olafsson B. Partially Observable Markov Decision Processes for Faster Object Recognition. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198632

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

University of Alberta

12. Bastani, Meysam. Model-Free Intelligent Diabetes Management Using Machine Learning.

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

URL: https://era.library.ualberta.ca/files/c12579s341

► Each patient with Type-1 diabetes must decide how much insulin to inject before each meal to maintain an acceptable level of blood glucose. The actual… (more)

Subjects/Keywords: reinforcement learning; policy gradient; diabetes; insulin dosage adjustment; supervised learning; machine learning; type-1 diabetes; actor-critic

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Bastani, M. (2013). Model-Free Intelligent Diabetes Management Using Machine Learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/c12579s341

Chicago Manual of Style (16^th Edition):

Bastani, Meysam. “Model-Free Intelligent Diabetes Management Using Machine Learning.” 2013. Masters Thesis, University of Alberta. Accessed January 24, 2021. https://era.library.ualberta.ca/files/c12579s341.

MLA Handbook (7^th Edition):

Bastani, Meysam. “Model-Free Intelligent Diabetes Management Using Machine Learning.” 2013. Web. 24 Jan 2021.

Vancouver:

Bastani M. Model-Free Intelligent Diabetes Management Using Machine Learning. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2021 Jan 24]. Available from: https://era.library.ualberta.ca/files/c12579s341.

Council of Science Editors:

Bastani M. Model-Free Intelligent Diabetes Management Using Machine Learning. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/c12579s341

Halmstad University

13. Olsson, Anton. Domain Transfer for End-to-end Reinforcement Learning.

Degree: Information Technology, 2020, Halmstad University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042

► In this master thesis project a LiDAR-based, depth image-based and semantic segmentation image-based reinforcement learning agent is investigated and compared forlearning in simulation and… (more)

Subjects/Keywords: Reinforcement Learning; Domain Transfer; Deep Deterministic Policy Gradient; Reinforcement Learning in Real-time; Computer Sciences; Datavetenskap (datalogi); Computer Engineering; Datorteknik

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Olsson, A. (2020). Domain Transfer for End-to-end Reinforcement Learning. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042

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

Chicago Manual of Style (16^th Edition):

Olsson, Anton. “Domain Transfer for End-to-end Reinforcement Learning.” 2020. Thesis, Halmstad University. Accessed January 24, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042.

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

MLA Handbook (7^th Edition):

Olsson, Anton. “Domain Transfer for End-to-end Reinforcement Learning.” 2020. Web. 24 Jan 2021.

Vancouver:

Olsson A. Domain Transfer for End-to-end Reinforcement Learning. [Internet] [Thesis]. Halmstad University; 2020. [cited 2021 Jan 24]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042.

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

Council of Science Editors:

Olsson A. Domain Transfer for End-to-end Reinforcement Learning. [Thesis]. Halmstad University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43042

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

University of Waterloo

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

Degree: 2020, University of Waterloo

URL: http://hdl.handle.net/10012/15851

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

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

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Pereira, Sahil. “Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments.” 2020. Thesis, University of Waterloo. Accessed January 24, 2021. http://hdl.handle.net/10012/15851.

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

MLA Handbook (7^th Edition):

Pereira, Sahil. “Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments.” 2020. Web. 24 Jan 2021.

Vancouver:

Pereira S. Stackelberg Multi-Agent Reinforcement Learning for Hierarchical Environments. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/10012/15851.

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

Council of Science Editors:

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

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

Australian National University

15. Aberdeen, Douglas. Policy-Gradient Algorithms for Partially Observable Markov Decision Processes .

Degree: 2003, Australian National University

URL: http://hdl.handle.net/1885/48180

► Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems, for example, robot navigation, driving a car,… (more)

Subjects/Keywords: POMDP; Reinforcement Learning; Policy gradient; cluster; high performance computing

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Aberdeen, D. (2003). Policy-Gradient Algorithms for Partially Observable Markov Decision Processes . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/48180

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

Chicago Manual of Style (16^th Edition):

Aberdeen, Douglas. “Policy-Gradient Algorithms for Partially Observable Markov Decision Processes .” 2003. Thesis, Australian National University. Accessed January 24, 2021. http://hdl.handle.net/1885/48180.

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

MLA Handbook (7^th Edition):

Aberdeen, Douglas. “Policy-Gradient Algorithms for Partially Observable Markov Decision Processes .” 2003. Web. 24 Jan 2021.

Vancouver:

Aberdeen D. Policy-Gradient Algorithms for Partially Observable Markov Decision Processes . [Internet] [Thesis]. Australian National University; 2003. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/1885/48180.

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

Council of Science Editors:

Aberdeen D. Policy-Gradient Algorithms for Partially Observable Markov Decision Processes . [Thesis]. Australian National University; 2003. Available from: http://hdl.handle.net/1885/48180

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

Australian National University

16. Greensmith, Evan. Policy Gradient Methods: Variance Reduction and Stochastic Convergence .

Degree: 2005, Australian National University

URL: http://hdl.handle.net/1885/47105

► In a reinforcement learning task an agent must learn a policy for performing actions so as to perform well in a given environment. Policy gradient… (more)

Subjects/Keywords: reinforcement learning; policy gradient; stochastic convergence; variance reduction

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Greensmith, E. (2005). Policy Gradient Methods: Variance Reduction and Stochastic Convergence . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/47105

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

Chicago Manual of Style (16^th Edition):

Greensmith, Evan. “Policy Gradient Methods: Variance Reduction and Stochastic Convergence .” 2005. Thesis, Australian National University. Accessed January 24, 2021. http://hdl.handle.net/1885/47105.

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

MLA Handbook (7^th Edition):

Greensmith, Evan. “Policy Gradient Methods: Variance Reduction and Stochastic Convergence .” 2005. Web. 24 Jan 2021.

Vancouver:

Greensmith E. Policy Gradient Methods: Variance Reduction and Stochastic Convergence . [Internet] [Thesis]. Australian National University; 2005. [cited 2021 Jan 24]. Available from: http://hdl.handle.net/1885/47105.

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

Council of Science Editors:

Greensmith E. Policy Gradient Methods: Variance Reduction and Stochastic Convergence . [Thesis]. Australian National University; 2005. Available from: http://hdl.handle.net/1885/47105

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

University of South Florida

17. Michaud, Brianna. A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution.

Degree: 2016, University of South Florida

URL: https://scholarcommons.usf.edu/etd/6543

► Between 1988 and 2014, otter trawls, seine nets, and plankton nets were deployed along the salinity gradients of 18 estuaries by the University of South… (more)

▼ Between 1988 and 2014, otter trawls, seine nets, and plankton nets were deployed along the salinity gradients of 18 estuaries by the University of South Florida and the Florida Fish and Wildlife Research Institute (FWRI, a research branch of the Florida Fish and Wildlife Conservation Commission). The purpose of these surveys was to document the responses of aquatic estuarine biota to variation in the quantity and quality of freshwater inflows that were being managed by the Southwest Florida Water Management District (SWFWMD). In the present analyses, four community types collected by these gears were compared with a diversity of habitat factors to identify the factors with the greatest influence on beta diversity, and also to identify the factors that were most influential to important prey species and economically important species. The four community types were (1) plankton-net invertebrates, (2) plankton-net ichthyoplankton, (3) seine nekton, and (4) trawl nekton. The habitat factors were (1) vertical profiles of salinity, dissolved oxygen, pH, and water temperature taken at the time of the biological collections, (2) various characterizations of local habitat associated with seine and trawl deployments, (3) chlorophyll a, color, and turbidity data obtained from the STORET database (US Environmental Protection Agency), and (4) data that characterize the effects of freshwater inflow on different estuarine zones, including factors for freshwater inflow, freshwater turnover time, and temporal instability in freshwater inflow (flashiness). Only 13 of the 18 estuaries had data that were comprehensive enough to allow habitat-factor analysis. An existing study had performed distance-based redundancy analysis (dbRDA) and principle component analysis (PCA) for these data within 78 estuarine survey zones that were composited together (i.e., regardless of estuary of origin). Based on that study’s findings, the communities of primarily spring-fed and primarily surface-fed estuaries were analyzed separately in the present study. Analysis was also performed with the habitat factors grouped into three categories (water management, restoration, and water quality) based on their ability to be directly modified by different management sectors. For an analysis of beta diversity interactions with habitat factors, dbRDA (called distance-based linear modeling (DistLM) in the PRIMER software) was performed using PRIMER 7 software (Quest Research Limited, Auckland, NZ). The dbRDA indicated pH, salinity, and distance to the Gulf of Mexico (distance-to-GOM) usually explained the most variation in the biotic data. These results were compared with partial dbRDA using the Akaike Information Criterion (AIC) as the model selection criterion with distance-to-GOM held as a covariate to reduce the effect of differences in the connectivity of marine-derived organisms to the different estuaries; distance-to-GOM explained between 8.46% and 32.4% of the variation in beta diversity. Even with the variation from distance-to-GOM removed, salinity was…

Subjects/Keywords: multivariate analysis; community; estuarine management; estuarine gradient; Natural Resources Management and Policy; Other Oceanography and Atmospheric Sciences and Meteorology

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Michaud, B. (2016). A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/6543

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

Chicago Manual of Style (16^th Edition):

Michaud, Brianna. “A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution.” 2016. Thesis, University of South Florida. Accessed January 24, 2021. https://scholarcommons.usf.edu/etd/6543.

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

MLA Handbook (7^th Edition):

Michaud, Brianna. “A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution.” 2016. Web. 24 Jan 2021.

Vancouver:

Michaud B. A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution. [Internet] [Thesis]. University of South Florida; 2016. [cited 2021 Jan 24]. Available from: https://scholarcommons.usf.edu/etd/6543.

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

Council of Science Editors:

Michaud B. A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution. [Thesis]. University of South Florida; 2016. Available from: https://scholarcommons.usf.edu/etd/6543

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

18. Aklil, Nassim. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.

Degree: Docteur es, Intelligence Artificielle et Robotique, 2017, Université Pierre et Marie Curie – Paris VI

URL: http://www.theses.fr/2017PA066225

►

La prise de décision est un domaine très étudié en sciences, que ce soit en neurosciences pour comprendre les processus sous tendant la prise de… (more)

▼

La prise de décision est un domaine très étudié en sciences, que ce soit en neurosciences pour comprendre les processus sous tendant la prise de décision chez les animaux, qu’en robotique pour modéliser des processus de prise de décision efficaces et rapides dans des tâches en environnement réel. En neurosciences, ce problème est résolu online avec des modèles de prises de décision séquentiels basés sur l’apprentissage par renforcement. En robotique, l’objectif premier est l’efficacité, dans le but d’être déployés en environnement réel. Cependant en robotique ce que l’on peut appeler le budget et qui concerne les limitations inhérentes au matériel, comme les temps de calculs, les actions limitées disponibles au robot ou la durée de vie de la batterie du robot, ne sont souvent pas prises en compte à l’heure actuelle. Nous nous proposons dans ce travail de thèse d’introduire la notion de budget comme contrainte explicite dans les processus d’apprentissage robotique appliqués à une tâche de localisation en mettant en place un modèle basé sur des travaux développés en apprentissage statistique qui traitent les données sous contrainte de budget, en limitant l’apport en données ou en posant une contrainte de temps plus explicite. Dans le but d’envisager un fonctionnement online de ce type d’algorithmes d’apprentissage budgétisé, nous discutons aussi certaines inspirations possibles qui pourraient être prises du côté des neurosciences computationnelles. Dans ce cadre, l’alternance entre recherche d’information pour la localisation et la décision de se déplacer pour un robot peuvent être indirectement liés à la notion de compromis exploration-exploitation. Nous présentons notre contribution à la modélisation de ce compromis chez l’animal dans une tâche non stationnaire impliquant différents niveaux d’incertitude, et faisons le lien avec les méthodes de bandits manchot.

Decision-making is a highly researched field in science, be it in neuroscience to understand the processes underlying animal decision-making, or in robotics to model efficient and rapid decision-making processes in real environments. In neuroscience, this problem is resolved online with sequential decision-making models based on reinforcement learning. In robotics, the primary objective is efficiency, in order to be deployed in real environments. However, in robotics what can be called the budget and which concerns the limitations inherent to the hardware, such as computation times, limited actions available to the robot or the lifetime of the robot battery, are often not taken into account at the present time. We propose in this thesis to introduce the notion of budget as an explicit constraint in the robotic learning processes applied to a localization task by implementing a model based on work developed in statistical learning that processes data under explicit constraints, limiting the input of data or imposing a more explicit time constraint. In order to discuss an online functioning of this type of budgeted learning algorithms, we also discuss some…

Advisors/Committee Members: Khamassi, Mehdi (thesis director), Girard, Benoît (thesis director), Denoyer, Ludovic (thesis director).

Subjects/Keywords: Apprentissage par renforcement; Apprentissage budgétisé; Apprentissage profond; Neurosciences computationnelles; Compromis exploration/exploitation; Policy gradient; Budgeted learning; Computational neuroscience; Deep learning; 629.89

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Aklil, N. (2017). Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. (Doctoral Dissertation). Université Pierre et Marie Curie – Paris VI. Retrieved from http://www.theses.fr/2017PA066225

Chicago Manual of Style (16^th Edition):

Aklil, Nassim. “Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.” 2017. Doctoral Dissertation, Université Pierre et Marie Curie – Paris VI. Accessed January 24, 2021. http://www.theses.fr/2017PA066225.

MLA Handbook (7^th Edition):

Aklil, Nassim. “Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment.” 2017. Web. 24 Jan 2021.

Vancouver:

Aklil N. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. [Internet] [Doctoral dissertation]. Université Pierre et Marie Curie – Paris VI; 2017. [cited 2021 Jan 24]. Available from: http://www.theses.fr/2017PA066225.

Council of Science Editors:

Aklil N. Apprentissage actif sous contrainte de budget en robotique et en neurosciences computationnelles. Localisation robotique et modélisation comportementale en environnement non stationnaire : Active learning under budget constraint in robotics and computational neuroscience. Robotic localization and behavioral modeling in non-stationary environment. [Doctoral Dissertation]. Université Pierre et Marie Curie – Paris VI; 2017. Available from: http://www.theses.fr/2017PA066225

Cal Poly

19. McDowell, Journey. Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks.

Degree: MS, Mechanical Engineering, 2019, Cal Poly

URL: https://digitalcommons.calpoly.edu/theses/2100 ; 10.15368/theses.2019.117

► Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations,… (more)

▼ Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn't the trailer it trained on. Advisors/Committee Members: Charles Birdsong.

Subjects/Keywords: Linear Quadratic Regulator; Deep Deterministic Policy Gradient; LQR; DDPG; Autonomous Vehicle; Machine Learning; Navigation, Guidance, Control, and Dynamics

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

McDowell, J. (2019). Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/2100 ; 10.15368/theses.2019.117

Chicago Manual of Style (16^th Edition):

McDowell, Journey. “Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks.” 2019. Masters Thesis, Cal Poly. Accessed January 24, 2021. https://digitalcommons.calpoly.edu/theses/2100 ; 10.15368/theses.2019.117.

MLA Handbook (7^th Edition):

McDowell, Journey. “Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks.” 2019. Web. 24 Jan 2021.

Vancouver:

McDowell J. Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks. [Internet] [Masters thesis]. Cal Poly; 2019. [cited 2021 Jan 24]. Available from: https://digitalcommons.calpoly.edu/theses/2100 ; 10.15368/theses.2019.117.

Council of Science Editors:

McDowell J. Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks. [Masters Thesis]. Cal Poly; 2019. Available from: https://digitalcommons.calpoly.edu/theses/2100 ; 10.15368/theses.2019.117

20. Bhojraj, Gokul Kaisaravalli. Policy-based Reinforcement learning control for window opening and closing in an office building.

Degree: Microdata Analysis, 2020, Dalarna University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:du-34420

► The level of indoor comfort can highly be influenced by window opening and closing behavior of the occupant in an office building. It will… (more)

Subjects/Keywords: Markov decision processes; Policy-based Reinforcement learning; Value-based Reinforcement learning; Q-learning; REINFORCE; policy gradient; window control; indoor comfort level; Social Sciences; Samhällsvetenskap

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Bhojraj, G. K. (2020). Policy-based Reinforcement learning control for window opening and closing in an office building. (Thesis). Dalarna University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:du-34420

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

Chicago Manual of Style (16^th Edition):

Bhojraj, Gokul Kaisaravalli. “Policy-based Reinforcement learning control for window opening and closing in an office building.” 2020. Thesis, Dalarna University. Accessed January 24, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34420.

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

MLA Handbook (7^th Edition):

Bhojraj, Gokul Kaisaravalli. “Policy-based Reinforcement learning control for window opening and closing in an office building.” 2020. Web. 24 Jan 2021.

Vancouver:

Bhojraj GK. Policy-based Reinforcement learning control for window opening and closing in an office building. [Internet] [Thesis]. Dalarna University; 2020. [cited 2021 Jan 24]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:du-34420.

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

Council of Science Editors:

Bhojraj GK. Policy-based Reinforcement learning control for window opening and closing in an office building. [Thesis]. Dalarna University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:du-34420

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

21. -2178-1988. Program analysis techniques for algorithmic complexity and relational properties.

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

URL: http://dx.doi.org/10.26153/tsw/2181

► Analyzing standard safety properties of a given program has traditionally been the primary focus of the program analysis community. Unfortunately, there are still many interesting… (more)

Subjects/Keywords: Complexity testing; Optimal program synthesis; Fuzzing; Genetic programming; Performance bug; Vulnerability detection; Side channel; Static analysis; Relational verification; Reinforcement learning; Policy gradient

10 more images…

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

-2178-1988. (2019). Program analysis techniques for algorithmic complexity and relational properties. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2181

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

Chicago Manual of Style (16^th Edition):

-2178-1988. “Program analysis techniques for algorithmic complexity and relational properties.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021. http://dx.doi.org/10.26153/tsw/2181.

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

MLA Handbook (7^th Edition):

-2178-1988. “Program analysis techniques for algorithmic complexity and relational properties.” 2019. Web. 24 Jan 2021.

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

Vancouver:

-2178-1988. Program analysis techniques for algorithmic complexity and relational properties. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 24]. Available from: http://dx.doi.org/10.26153/tsw/2181.

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

Council of Science Editors:

-2178-1988. Program analysis techniques for algorithmic complexity and relational properties. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2181

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

22. 小倉, 裕平. 運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究.

Degree: Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学

URL: http://hdl.handle.net/10119/15138

Supervisor:Ho Bao Tu

先端科学技術研究科

修士（知識科学）

Subjects/Keywords: 強化学習; Reinforcement Learning; ランニング経路推薦; Running Route Recommendation; 方策勾配法; Policy Gradient

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

小倉, �. (n.d.). 運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究. (Thesis). Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10119/15138

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

小倉, 裕平. “運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究.” Thesis, Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学. Accessed January 24, 2021. http://hdl.handle.net/10119/15138.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

小倉, 裕平. “運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究.” Web. 24 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

小倉 �. 運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究. [Internet] [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; [cited 2021 Jan 24]. Available from: http://hdl.handle.net/10119/15138.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

小倉 �. 運動者に対するランニング経路推薦のための方策勾配法に基づくランニング経路生成方法の研究. [Thesis]. Japan Advanced Institute of Science and Technology / 北陸先端科学技術大学院大学; Available from: http://hdl.handle.net/10119/15138

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

23. Van der Spek, I.T. (author). Imitation learning for a robotic precision placement task.

Degree: 2014, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:86815e55-bbba-45b4-915b-6f321b485940

►

In industrial environments robots are used for various tasks. At this moment it is not feasible for companies to deploy robots for productions with a… (more)

▼

In industrial environments robots are used for various tasks. At this moment it is not feasible for companies to deploy robots for productions with a limited batch size or for products with large variations. The use of robots for such environments can become feasible through a new generation of robots and software which can adapt quickly to new situations and learn from their mistakes while being programmable without needing an expert. A concept that can enable the transition to flexible robotics is the combination of imitation learning and reinforcement learning. The purpose of imitation learning is to learn a task by generalizing from observations. The power of imitation learning is that the robot is programmed in an intuitive way while the insight of the teacher is incorporated in the execution of the task. This research studies the combination of imitation and reinforcement learning, the research is applied to an industrial use-case. The research question of this study is: "Can imitation learning be combined with reinforcement learning to achieve a successful application in an industrial robotic precision placement task?" To imitate the demonstrated trajectories, Dynamic Movement Primitives (DMPs) are used. The DMPs are used to encode the observed trajectories. DMPs can be seen as a spring-damper like system with a non-linear forcing term. The forcing term is a sum of Gaussian basis functions with each its corresponding weight. Reinforcement learning can be applied to these weights to alter the shape of the trajectory created by a DMP. Policy Gradients with Parameter based Exploration (PGPE) is used as reinforcement learning algorithm to optimize the recorded trajectories. Experiments done on a UR5 show that without the learning step, the DMPs are able to provide a trajectory that results in a successful execution of a robotic precision placement task. The experiments also show that the learning algorithm is not able to remove noise from a demonstrated trajectory or complete a partial demonstrated trajectory. Therefore it can be concluded that the PGPE algorithm is not suited for reinforcement learning in robotics in its current form. It is therefore recommended to apply a data-efficient version of the PGPE algorithm in order to achieve better learning results.

Embedded Systems

Delft Center for Systems and Control

Mechanical, Maritime and Materials Engineering

Advisors/Committee Members: Babuska, R. (mentor), Kuijpers, J. (mentor).

Subjects/Keywords: reinforcement learning; imitation learning; policy gradient; pgpe; dynamic movement primitives; precision placement; dmp; optimization

…third method is the use of a policy gradient method: Policy Gradients with Parameter based… …contributions to the existing work. The first contribution of this thesis is the policy gradient… …algorithm which has not been combined with imitation learning before. The policy gradient… …used for encoding trajectories. Subsequently in Chapter 4 the policy gradient algorithm is… …gradient approaches based on likelihood-ratio estimation, 2) policy updates inspired by…

10 more images…

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Van der Spek, I. T. (. (2014). Imitation learning for a robotic precision placement task. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:86815e55-bbba-45b4-915b-6f321b485940

Chicago Manual of Style (16^th Edition):

Van der Spek, I T (author). “Imitation learning for a robotic precision placement task.” 2014. Masters Thesis, Delft University of Technology. Accessed January 24, 2021. http://resolver.tudelft.nl/uuid:86815e55-bbba-45b4-915b-6f321b485940.

MLA Handbook (7^th Edition):

Van der Spek, I T (author). “Imitation learning for a robotic precision placement task.” 2014. Web. 24 Jan 2021.

Vancouver:

Van der Spek IT(. Imitation learning for a robotic precision placement task. [Internet] [Masters thesis]. Delft University of Technology; 2014. [cited 2021 Jan 24]. Available from: http://resolver.tudelft.nl/uuid:86815e55-bbba-45b4-915b-6f321b485940.

Council of Science Editors:

Van der Spek IT(. Imitation learning for a robotic precision placement task. [Masters Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:86815e55-bbba-45b4-915b-6f321b485940

24. Masood, Muhammad Arjumand. Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning.

Degree: PhD, 2019, Harvard University

URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029756

►

Machine Learning problems often admit a solution space that is not unique. When multiple feasible solutions exist, picking from a diverse, representative set may lead… (more)

Subjects/Keywords: machine learning; NMF; non-negative matrix factorization; reinforcement learning; policy gradient; Stein discrepancy

…augmenting existing state of the art policy gradient methods for RL with a diversity-inducing term… …NMF) as well as an extension to on-policy and off-policy Reinforcement learning (… …Learning). The goal in Reinforcement Learning is to learn a policy (action selection… …This technique requires direct access to the environment (on-policy RL setting) in… …order to simulate the performance of the policy being optimized. In Chapter 7, we introduce a…

10 more images…

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Masood, M. A. (2019). Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029756

Chicago Manual of Style (16^th Edition):

Masood, Muhammad Arjumand. “Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning.” 2019. Doctoral Dissertation, Harvard University. Accessed January 24, 2021. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029756.

MLA Handbook (7^th Edition):

Masood, Muhammad Arjumand. “Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning.” 2019. Web. 24 Jan 2021.

Vancouver:

Masood MA. Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning. [Internet] [Doctoral dissertation]. Harvard University; 2019. [cited 2021 Jan 24]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029756.

Council of Science Editors:

Masood MA. Algorithms for Discovering Collections of High-Quality and Diverse Solutions, With Applications to Bayesian Non-Negative Matrix Factorization and Reinforcement Learning. [Doctoral Dissertation]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029756

25. Liu, Stewart. Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization.

Degree: Industrial Engineering & Operations Research, 2017, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/3n52p4zj

► In initial work, we found a version of Retrospective Optimization, in which we optimize over a single randomly generated long sample path, is often effective… (more)

Subjects/Keywords: Operations research; Data-Driven Optimization; Gradient Search; Multi-Echelon Supply Chain; Retrospective Optimization; State-Dependent Inventory Policy; Supply Chain Optimization

…for Hybrid Retrospective Optimization and Gradient Search In general, we seek the policy… …Per Period, Normal(32, 8) . . . . . . . . . . Gradient Search : Init = 30 period… …Gradient Search Gap . . . . . . . . . . . . . . . . Stochastic Lead Times, σ = 0.3… …Effects of Gradient Estimation Step Sizes . . . . . Tradeoff between Step Size and Starting… …approximate solution algorithm that utilizes a combination of traditional MILP solver and gradient…

11 more images…

Record Details Similar Records Cite « Share

❌

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

APA (6^th Edition):

Liu, S. (2017). Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/3n52p4zj

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

Chicago Manual of Style (16^th Edition):

Liu, Stewart. “Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization.” 2017. Thesis, University of California – Berkeley. Accessed January 24, 2021. http://www.escholarship.org/uc/item/3n52p4zj.

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

MLA Handbook (7^th Edition):

Liu, Stewart. “Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization.” 2017. Web. 24 Jan 2021.

Vancouver:

Liu S. Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization. [Internet] [Thesis]. University of California – Berkeley; 2017. [cited 2021 Jan 24]. Available from: http://www.escholarship.org/uc/item/3n52p4zj.

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

Council of Science Editors:

Liu S. Combining Retrospective Optimization and Gradient Search for Supply Chain Optimization. [Thesis]. University of California – Berkeley; 2017. Available from: http://www.escholarship.org/uc/item/3n52p4zj

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

		in
And / Or
		in
And / Or
		in
And / Or
		in

Open Access Theses and Dissertations