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You searched for subject:(Modular reinforcement learning). Showing records 1 – 6 of 6 total matches.

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University of Texas – Austin

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

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

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

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

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Masters Thesis, University of Texas – Austin. Accessed October 20, 2020. http://hdl.handle.net/2152/46987.

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

MLA Handbook (7th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Web. 20 Oct 2020.

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

Vancouver:

-8073-3276. Parameterized modular inverse reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/2152/46987.

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

Council of Science Editors:

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

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

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

Degree: PhD, 2000, University of Sussex

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Doctoral Dissertation, University of Sussex. Accessed October 20, 2020. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189.

MLA Handbook (7th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Web. 20 Oct 2020.

Vancouver:

Paraskevopoulos V. Design of optimal neural network control strategies with minimal a priori knowledge. [Internet] [Doctoral dissertation]. University of Sussex; 2000. [cited 2020 Oct 20]. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189.

Council of Science Editors:

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

3. Heikkilä, Filip. Autonomous Mapping of Unknown Environments Using a UAV .

Degree: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper, 2020, Chalmers University of Technology

 Automatic object search in a bounded area can be accomplished using cameracarrying autonomous aerial robots. The system requires several functionalities to solve the task in… (more)

Subjects/Keywords: Deep reinforcement learning; autonomous exploration and navigation; feature extraction; object detection; voxel map; UAV; modular framework.

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

APA (6th Edition):

Heikkilä, F. (2020). Autonomous Mapping of Unknown Environments Using a UAV . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300894

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):

Heikkilä, Filip. “Autonomous Mapping of Unknown Environments Using a UAV .” 2020. Thesis, Chalmers University of Technology. Accessed October 20, 2020. http://hdl.handle.net/20.500.12380/300894.

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

MLA Handbook (7th Edition):

Heikkilä, Filip. “Autonomous Mapping of Unknown Environments Using a UAV .” 2020. Web. 20 Oct 2020.

Vancouver:

Heikkilä F. Autonomous Mapping of Unknown Environments Using a UAV . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/20.500.12380/300894.

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

Council of Science Editors:

Heikkilä F. Autonomous Mapping of Unknown Environments Using a UAV . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/300894

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

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

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

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

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

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

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

APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Masters Thesis, University of Texas – Austin. Accessed October 20, 2020. http://hdl.handle.net/2152/25916.

MLA Handbook (7th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Web. 20 Oct 2020.

Vancouver:

Zhang R. Action selection in modular reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/2152/25916.

Council of Science Editors:

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

5. Simpkins, Christopher Lee. Integrating reinforcement learning into a programming language.

Degree: PhD, Computer Science, 2017, Georgia Tech

Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to… (more)

Subjects/Keywords: Machine learning; Reinforcement learning; Modular reinforcement learning; Programming languages; Domain specific languages; Software engineering; Artificial intelligence; Intelligent agents

…Curse of Dimensionality in Reinforcement Learning . . . 20 2.2.3 Modular Reinforcement… …Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . 25 Modular reinforcement… …Modular reinforcement learning, as it is called in the literature, decomposes an agent into… …a command arbitration algorithm for modular reinforcement learning that enables… …x29; – that integrates modular reinforcement learning in a way that allows programmers to… 

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

APA (6th Edition):

Simpkins, C. L. (2017). Integrating reinforcement learning into a programming language. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58683

Chicago Manual of Style (16th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2020. http://hdl.handle.net/1853/58683.

MLA Handbook (7th Edition):

Simpkins, Christopher Lee. “Integrating reinforcement learning into a programming language.” 2017. Web. 20 Oct 2020.

Vancouver:

Simpkins CL. Integrating reinforcement learning into a programming language. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/1853/58683.

Council of Science Editors:

Simpkins CL. Integrating reinforcement learning into a programming language. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58683

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

Degree: 2019, Université de Montréal

Subjects/Keywords: Reinforcement Learning; Hierarchical Reinforcement Learning; Information Bottleneck; Compositionality; Modular network; Apprentissage par renforcement; Apprentissage par renforcement hiérarchique; Goulot d'étranglement de l'information; Compositionnalité; Réseaux modulaires; Applied Sciences - Artificial Intelligence / Sciences appliqués et technologie - Intelligence artificielle (UMI : 0800)

…beyond Hierarchical Reinforcement Learning and is studied under the paradigm of Neural Modular… …HRL Hierarchical Reinforcement Learning LSTM Long-Short Term Memory MDP Markov Decision… …Process NMN Neural Module Network PVF Proto-Value Functions RL Reinforcement Learning RNN… …reinforcement learning algorithms that can quickly adapt to new tasks by obtaining a structured… …Hierarchical Reinforcement Learning. We review this body of literature in section 1.3 and describe… 

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Sodhani, Shagun. “Learning competitive ensemble of information-constrained primitives.” 2019. Thesis, Université de Montréal. Accessed October 20, 2020. http://hdl.handle.net/1866/22537.

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

MLA Handbook (7th Edition):

Sodhani, Shagun. “Learning competitive ensemble of information-constrained primitives.” 2019. Web. 20 Oct 2020.

Vancouver:

Sodhani S. Learning competitive ensemble of information-constrained primitives. [Internet] [Thesis]. Université de Montréal; 2019. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/1866/22537.

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

Council of Science Editors:

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

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

.