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You searched for subject:(Policy Iteration). Showing records 1 – 14 of 14 total matches.

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University of Alberta

1. Yao,Hengshuai. Model-based Reinforcement Learning with State and Action Abstractions.

Degree: PhD, Department of Computing Science, 2016, University of Alberta

 In model-based reinforcement learning a model is learned which is then used to find good actions. What model to learn? We investigate these questions in… (more)

Subjects/Keywords: Approximate policy iteration,; reinforcement learning; planning; Approximate value iteration; MDPs

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APA (6th Edition):

Yao,Hengshuai. (2016). Model-based Reinforcement Learning with State and Action Abstractions. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cw9505055r

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

Chicago Manual of Style (16th Edition):

Yao,Hengshuai. “Model-based Reinforcement Learning with State and Action Abstractions.” 2016. Doctoral Dissertation, University of Alberta. Accessed November 21, 2019. https://era.library.ualberta.ca/files/cw9505055r.

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

MLA Handbook (7th Edition):

Yao,Hengshuai. “Model-based Reinforcement Learning with State and Action Abstractions.” 2016. Web. 21 Nov 2019.

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

Vancouver:

Yao,Hengshuai. Model-based Reinforcement Learning with State and Action Abstractions. [Internet] [Doctoral dissertation]. University of Alberta; 2016. [cited 2019 Nov 21]. Available from: https://era.library.ualberta.ca/files/cw9505055r.

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

Council of Science Editors:

Yao,Hengshuai. Model-based Reinforcement Learning with State and Action Abstractions. [Doctoral Dissertation]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cw9505055r

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


Case Western Reserve University

2. Schwab, Devin. Hierarchical Sampling for Least-Squares Policy Iteration.

Degree: MSs, EECS - Computer and Information Sciences, 2016, Case Western Reserve University

 For large Sequential Decision Making tasks, an agent may need to make lots of exploratory interactions within the environment in order to learn the optimal… (more)

Subjects/Keywords: Computer Science; reinforcement learning; MaxQ; LSPI; Least-Squares Policy Iteration

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APA (6th Edition):

Schwab, D. (2016). Hierarchical Sampling for Least-Squares Policy Iteration. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1441374844

Chicago Manual of Style (16th Edition):

Schwab, Devin. “Hierarchical Sampling for Least-Squares Policy Iteration.” 2016. Masters Thesis, Case Western Reserve University. Accessed November 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1441374844.

MLA Handbook (7th Edition):

Schwab, Devin. “Hierarchical Sampling for Least-Squares Policy Iteration.” 2016. Web. 21 Nov 2019.

Vancouver:

Schwab D. Hierarchical Sampling for Least-Squares Policy Iteration. [Internet] [Masters thesis]. Case Western Reserve University; 2016. [cited 2019 Nov 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1441374844.

Council of Science Editors:

Schwab D. Hierarchical Sampling for Least-Squares Policy Iteration. [Masters Thesis]. Case Western Reserve University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1441374844

3. Hyytiä, Esa. Resource Allocation and Performance Analysis Problems in Optical Networks.

Degree: 2004, Helsinki University of Technology

 Optical networks pose a rich variety of new design and performance analysis problems. Typically, the static design problems belong to the field of combinatorial optimisation,… (more)

Subjects/Keywords: optical networks; WDM; routing and wavelength assignment; Markov decision process; policy iteration; optical burst switching

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APA (6th Edition):

Hyytiä, E. (2004). Resource Allocation and Performance Analysis Problems in Optical Networks. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2004/isbn9512274051/

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):

Hyytiä, Esa. “Resource Allocation and Performance Analysis Problems in Optical Networks.” 2004. Thesis, Helsinki University of Technology. Accessed November 21, 2019. http://lib.tkk.fi/Diss/2004/isbn9512274051/.

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

MLA Handbook (7th Edition):

Hyytiä, Esa. “Resource Allocation and Performance Analysis Problems in Optical Networks.” 2004. Web. 21 Nov 2019.

Vancouver:

Hyytiä E. Resource Allocation and Performance Analysis Problems in Optical Networks. [Internet] [Thesis]. Helsinki University of Technology; 2004. [cited 2019 Nov 21]. Available from: http://lib.tkk.fi/Diss/2004/isbn9512274051/.

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

Council of Science Editors:

Hyytiä E. Resource Allocation and Performance Analysis Problems in Optical Networks. [Thesis]. Helsinki University of Technology; 2004. Available from: http://lib.tkk.fi/Diss/2004/isbn9512274051/

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


University of Alberta

4. Farahmand, Amir-massoud. Regularization in reinforcement learning.

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

 This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and… (more)

Subjects/Keywords: Statistical Learning Theory; Regularized Least-Squares Regression; Regularized Fitted Q-Iteration; Regularized LSTD; Regularized Policy Iteration; Approximate Value/Policy Iteration; Error Propagation; Regularization; Model Selection; Machine Learning; Reinforcement Learning; Sequential Decision-Making Problems

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APA (6th Edition):

Farahmand, A. (2011). Regularization in reinforcement learning. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/pn89d6788

Chicago Manual of Style (16th Edition):

Farahmand, Amir-massoud. “Regularization in reinforcement learning.” 2011. Doctoral Dissertation, University of Alberta. Accessed November 21, 2019. https://era.library.ualberta.ca/files/pn89d6788.

MLA Handbook (7th Edition):

Farahmand, Amir-massoud. “Regularization in reinforcement learning.” 2011. Web. 21 Nov 2019.

Vancouver:

Farahmand A. Regularization in reinforcement learning. [Internet] [Doctoral dissertation]. University of Alberta; 2011. [cited 2019 Nov 21]. Available from: https://era.library.ualberta.ca/files/pn89d6788.

Council of Science Editors:

Farahmand A. Regularization in reinforcement learning. [Doctoral Dissertation]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/pn89d6788

5. Alizadeh, Pegah. Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues.

Degree: Docteur es, Informatique, 2016, Sorbonne Paris Cité

Les processus décisionnels de Markov (MDPs) modélisent des problèmes de décisionsséquentielles dans lesquels un utilisateur interagit avec l’environnement et adapte soncomportement en prenant en compte… (more)

Subjects/Keywords: Processus décisionnel de Markov; Valeur vectorielle MDP; Markov decision process; Vector-valued MPD; Policy iteration; Reward elicitation

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APA (6th Edition):

Alizadeh, P. (2016). Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2016USPCD011

Chicago Manual of Style (16th Edition):

Alizadeh, Pegah. “Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues.” 2016. Doctoral Dissertation, Sorbonne Paris Cité. Accessed November 21, 2019. http://www.theses.fr/2016USPCD011.

MLA Handbook (7th Edition):

Alizadeh, Pegah. “Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues.” 2016. Web. 21 Nov 2019.

Vancouver:

Alizadeh P. Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2016. [cited 2019 Nov 21]. Available from: http://www.theses.fr/2016USPCD011.

Council of Science Editors:

Alizadeh P. Elicitation and planning in Markov decision processes with unknown rewards : Elicitation et planification dans les processus décisionnel de MARKOV avec récompenses inconnues. [Doctoral Dissertation]. Sorbonne Paris Cité; 2016. Available from: http://www.theses.fr/2016USPCD011


Université Catholique de Louvain

6. Hollanders, Romain. Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration.

Degree: 2015, Université Catholique de Louvain

How to make the best decision in a complex environment is a question that has haunted many generations of researchers and practitioners. It has given… (more)

Subjects/Keywords: Policy Iteration; Markov Decision Process; Acyclic Unique Sink Orientation; Two-Player Turn-Based Stochastic Game; Algorithmic Complexity

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APA (6th Edition):

Hollanders, R. (2015). Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/169209

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):

Hollanders, Romain. “Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration.” 2015. Thesis, Université Catholique de Louvain. Accessed November 21, 2019. http://hdl.handle.net/2078.1/169209.

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

MLA Handbook (7th Edition):

Hollanders, Romain. “Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration.” 2015. Web. 21 Nov 2019.

Vancouver:

Hollanders R. Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration. [Internet] [Thesis]. Université Catholique de Louvain; 2015. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/2078.1/169209.

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

Council of Science Editors:

Hollanders R. Decision making in large stochastic and adversarial environments : a complexity analysis of Policy Iteration. [Thesis]. Université Catholique de Louvain; 2015. Available from: http://hdl.handle.net/2078.1/169209

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


University of Waterloo

7. Han, Dong. Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations.

Degree: 2011, University of Waterloo

 We propose multigrid methods for solving Hamilton-Jacobi-Bellman (HJB) and Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations. The methods are based on the full approximation scheme. We propose a damped-relaxation… (more)

Subjects/Keywords: multigrid methods; full approximation scheme; relaxation scheme; policy iteration; Hamilton-Jacobi-Bellman Equations; Hamilton-Jacobi-Bellman-Isaacs Equations; jump in control

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APA (6th Edition):

Han, D. (2011). Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6021

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):

Han, Dong. “Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations.” 2011. Thesis, University of Waterloo. Accessed November 21, 2019. http://hdl.handle.net/10012/6021.

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

MLA Handbook (7th Edition):

Han, Dong. “Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations.” 2011. Web. 21 Nov 2019.

Vancouver:

Han D. Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations. [Internet] [Thesis]. University of Waterloo; 2011. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/10012/6021.

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

Council of Science Editors:

Han D. Multigrid Methods for Hamilton-Jacobi-Bellman and Hamilton-Jacobi-Bellman-Isaacs Equations. [Thesis]. University of Waterloo; 2011. Available from: http://hdl.handle.net/10012/6021

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

8. Thiéry, Christophe. Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris.

Degree: Docteur es, Informatique, 2010, Université Henri Poincaré – Nancy I

Cette thèse s'intéresse aux méthodes d'itération sur les politiques dans l'apprentissage par renforcement à grand espace d'états avec approximation linéaire de la fonction de valeur.… (more)

Subjects/Keywords: Contrôle optimal stochastique; Apprentissage par renforcement; Programmation dynamique; Processus Décisionnels de Markov; Least-Squares Policy Iteration; [lambda]-Policy Iteration; Approximation de la fonction de valeur; Compromis biais-variance, Fonctions de base; Tetris; Méthode d'entropie croisée

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APA (6th Edition):

Thiéry, C. (2010). Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris. (Doctoral Dissertation). Université Henri Poincaré – Nancy I. Retrieved from http://www.theses.fr/2010NAN10128

Chicago Manual of Style (16th Edition):

Thiéry, Christophe. “Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris.” 2010. Doctoral Dissertation, Université Henri Poincaré – Nancy I. Accessed November 21, 2019. http://www.theses.fr/2010NAN10128.

MLA Handbook (7th Edition):

Thiéry, Christophe. “Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris.” 2010. Web. 21 Nov 2019.

Vancouver:

Thiéry C. Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris. [Internet] [Doctoral dissertation]. Université Henri Poincaré – Nancy I; 2010. [cited 2019 Nov 21]. Available from: http://www.theses.fr/2010NAN10128.

Council of Science Editors:

Thiéry C. Itération sur les politiques optimiste et apprentissage du jeu de Tetris : Optimistic Policy Iteration and Learning the Game of Tetris. [Doctoral Dissertation]. Université Henri Poincaré – Nancy I; 2010. Available from: http://www.theses.fr/2010NAN10128

9. Roux, Pierre. Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants.

Degree: Docteur es, Sureté de logiciel et calcul de haute performance, 2013, Toulouse, ISAE

Les systèmes critiques comme les commandes de vol peuvent entraîner des désastres en cas de dysfonctionnement. D'où l'intérêt porté à la fois par le monde… (more)

Subjects/Keywords: Interprétation abstraite; Systèmes de contrôle-commande; Invariants quadratiques; Itération sur les stratégies; Ellipsoïdes; Programmation semi-définie; Abstract interpretation; Abstract interpretation; Quadratic invariants; Policy iteration; Ellipsoids; Semi-definite programming; 000

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APA (6th Edition):

Roux, P. (2013). Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants. (Doctoral Dissertation). Toulouse, ISAE. Retrieved from http://www.theses.fr/2013ESAE0046

Chicago Manual of Style (16th Edition):

Roux, Pierre. “Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants.” 2013. Doctoral Dissertation, Toulouse, ISAE. Accessed November 21, 2019. http://www.theses.fr/2013ESAE0046.

MLA Handbook (7th Edition):

Roux, Pierre. “Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants.” 2013. Web. 21 Nov 2019.

Vancouver:

Roux P. Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants. [Internet] [Doctoral dissertation]. Toulouse, ISAE; 2013. [cited 2019 Nov 21]. Available from: http://www.theses.fr/2013ESAE0046.

Council of Science Editors:

Roux P. Analyse statique de systèmes de contrôle commande : synthèse d'invariants non linéaires : Static Analysis of Control Command Systems : Synthesizing non Linear Invariants. [Doctoral Dissertation]. Toulouse, ISAE; 2013. Available from: http://www.theses.fr/2013ESAE0046

10. Huang, Yiqing. Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem.

Degree: 2011, University of Waterloo

 Guaranteed Minimum Withdrawal Benefits(GMWB) have become popular riders on variable annuities. The pricing of a GMWB contract was originally formulated as a singular stochastic control… (more)

Subjects/Keywords: singular stochastic control; HJB equation; GMWB; iterative methods; scaled direct control method; penalty method; xed point policy iteration; policy iteration; jump diffusion; inexact arithmetic

…66 6 Fixed Point Policy Iteration 6.1 67 Methods for Solving Algebraic Equations… …Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.1.4… …Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.1.6 Fixed Point-Policy… …Policy Iteration . . . . . . . . . . . . . . . 73 6.3 Full Matrix Fixed Point-Policy… …96 6.4 6.5 7 Fixed Point Policy Iteration: Numerical Results 7.1 98 98 7.1.1 No… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Huang, Y. (2011). Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6109

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):

Huang, Yiqing. “Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem.” 2011. Thesis, University of Waterloo. Accessed November 21, 2019. http://hdl.handle.net/10012/6109.

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

MLA Handbook (7th Edition):

Huang, Yiqing. “Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem.” 2011. Web. 21 Nov 2019.

Vancouver:

Huang Y. Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem. [Internet] [Thesis]. University of Waterloo; 2011. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/10012/6109.

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

Council of Science Editors:

Huang Y. Numerical Methods for Pricing a Guaranteed Minimum Withdrawal Benefit (GMWB) as a Singular Control Problem. [Thesis]. University of Waterloo; 2011. Available from: http://hdl.handle.net/10012/6109

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

11. WENG RENRONG. Natural gas supply management and contract allocation and valuation for a power generation company.

Degree: 2015, National University of Singapore

Subjects/Keywords: Natural gas supply; contract allocation and valuation; contract pricing; multi-time scale Markov decision process; least-squares policy iteration.

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APA (6th Edition):

RENRONG, W. (2015). Natural gas supply management and contract allocation and valuation for a power generation company. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/119788

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):

RENRONG, WENG. “Natural gas supply management and contract allocation and valuation for a power generation company.” 2015. Thesis, National University of Singapore. Accessed November 21, 2019. http://scholarbank.nus.edu.sg/handle/10635/119788.

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

MLA Handbook (7th Edition):

RENRONG, WENG. “Natural gas supply management and contract allocation and valuation for a power generation company.” 2015. Web. 21 Nov 2019.

Vancouver:

RENRONG W. Natural gas supply management and contract allocation and valuation for a power generation company. [Internet] [Thesis]. National University of Singapore; 2015. [cited 2019 Nov 21]. Available from: http://scholarbank.nus.edu.sg/handle/10635/119788.

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

Council of Science Editors:

RENRONG W. Natural gas supply management and contract allocation and valuation for a power generation company. [Thesis]. National University of Singapore; 2015. Available from: http://scholarbank.nus.edu.sg/handle/10635/119788

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


Delft University of Technology

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

Degree: 2018, Delft University of Technology

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Junell, J L. “An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles.” 2018. Doctoral Dissertation, Delft University of Technology. Accessed November 21, 2019. http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294.

MLA Handbook (7th Edition):

Junell, J L. “An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles.” 2018. Web. 21 Nov 2019.

Vancouver:

Junell JL. An Empirical Approach to Reinforcement Learning for Micro Aerial Vehicles. [Internet] [Doctoral dissertation]. Delft University of Technology; 2018. [cited 2019 Nov 21]. Available from: http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; 32765560-5fde-4c86-a778-decdc3eb5294 ; 10.4233/uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; urn:isbn:978-94-6186-965-4 ; urn:NBN:nl:ui:24-uuid:32765560-5fde-4c86-a778-decdc3eb5294 ; http://resolver.tudelft.nl/uuid:32765560-5fde-4c86-a778-decdc3eb5294.

Council of Science Editors:

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

13. Goeringer, Tyler. Massively Parallel Reinforcement Learning With an Application to Video Games.

Degree: MSs, EECS - Computer and Information Sciences, 2013, Case Western Reserve University

 We propose a framework for periodic policy updates of computer controlled agents in an interactive scenario. We use the graphics processing unit (GPU) to accelerate… (more)

Subjects/Keywords: Computer Science; gpu; artificial intelligence; parallel programming; reinforcement learning; machine learning; least-squares policy iteration; lspi; quake iii

…Processing Unit HTN: Hierarchical Task Network LSPI: Least-Squares Policy Iteration MDP: Markov… …learning and the basic policy iteration algorithm, followed by a discussion of GPU architectures… …policy iteration algorithm to determine an optimal policy. The policy iteration algorithm… …requires some initial policy π0 followed by the repeated iteration of two 6 steps: policy… …policy, we must examine how the utility function is generated during each iteration. For… 

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

APA (6th Edition):

Goeringer, T. (2013). Massively Parallel Reinforcement Learning With an Application to Video Games. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1373073319

Chicago Manual of Style (16th Edition):

Goeringer, Tyler. “Massively Parallel Reinforcement Learning With an Application to Video Games.” 2013. Masters Thesis, Case Western Reserve University. Accessed November 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1373073319.

MLA Handbook (7th Edition):

Goeringer, Tyler. “Massively Parallel Reinforcement Learning With an Application to Video Games.” 2013. Web. 21 Nov 2019.

Vancouver:

Goeringer T. Massively Parallel Reinforcement Learning With an Application to Video Games. [Internet] [Masters thesis]. Case Western Reserve University; 2013. [cited 2019 Nov 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1373073319.

Council of Science Editors:

Goeringer T. Massively Parallel Reinforcement Learning With an Application to Video Games. [Masters Thesis]. Case Western Reserve University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1373073319

14. Yershov, Dmytro. Fast numerical algorithms for optimal robot motion planning.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 Optimization of high-level autonomous tasks requires solving the optimal motion planning problem for a mobile robot. For example, to reach the desired destination on time,… (more)

Subjects/Keywords: Optimal Motion Planning; Hamilton-Jacobi-Bellman; Numerical methods; Fast Marching Method; Simplicial Discretization; Simplicial Dijkstra Algorithm; Simplicial A* Algorithm; Simplicial Label Correcting Algorithm; Simplicial Value Iteration Algorithm; Simplicial Policy Iteration Algorithm; Mobile Robots; Robotics; Control; Optimal Control; Feedback Control; Obstacles; Shortest Path Problem; Weighted Region Problem; Differential Constraints; Nonholonomic Constraints; Stochastic Control; Stochastic Shortest Path Problem; Nearby Deterministic System; Turing Decidability; Turing Semidecidability; Sampling Metric Spaces; Resolution Completeness

…or policy iteration algorithms are required to solve the discretized problem. Similar to… …while FSM require iteration until convergence. Semi-Lagrangian Methods Generally, finite… …the causality property may not hold for the discretized problem. Therefore, value iteration… …equation for the cost-to-go function. This reduction provides the optimal feedback control policy… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yershov, D. (2014). Fast numerical algorithms for optimal robot motion planning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46659

Chicago Manual of Style (16th Edition):

Yershov, Dmytro. “Fast numerical algorithms for optimal robot motion planning.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed November 21, 2019. http://hdl.handle.net/2142/46659.

MLA Handbook (7th Edition):

Yershov, Dmytro. “Fast numerical algorithms for optimal robot motion planning.” 2014. Web. 21 Nov 2019.

Vancouver:

Yershov D. Fast numerical algorithms for optimal robot motion planning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Nov 21]. Available from: http://hdl.handle.net/2142/46659.

Council of Science Editors:

Yershov D. Fast numerical algorithms for optimal robot motion planning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46659

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