subject:(Modular inverse reinforcement learning)

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 (6^th 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 (16^th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Masters Thesis, University of Texas – Austin. Accessed March 07, 2021. 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 (7^th Edition):

-8073-3276. “Parameterized modular inverse reinforcement learning.” 2015. Web. 07 Mar 2021.

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 2021 Mar 07]. Available from: http://hdl.handle.net/2152/46987.

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

Council of Science Editors:

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

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

Rice University

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

Degree: MS, Engineering, 2018, Rice University

URL: http://hdl.handle.net/1911/105893

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

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

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Daptardar, Saurabh. “The Science of Mind Reading: New Inverse Optimal Control Framework.” 2018. Masters Thesis, Rice University. Accessed March 07, 2021. http://hdl.handle.net/1911/105893.

MLA Handbook (7^th Edition):

Daptardar, Saurabh. “The Science of Mind Reading: New Inverse Optimal Control Framework.” 2018. Web. 07 Mar 2021.

Vancouver:

Daptardar S. The Science of Mind Reading: New Inverse Optimal Control Framework. [Internet] [Masters thesis]. Rice University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1911/105893.

Council of Science Editors:

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

University of Illinois – Urbana-Champaign

3. Zaytsev, Andrey. Faster apprenticeship learning through inverse optimal control.

Degree: MS, Computer Science, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99228

► One of the fundamental problems of artificial intelligence is learning how to behave optimally. With applications ranging from self-driving cars to medical devices, this task… (more)

Subjects/Keywords: Apprenticeship learning; Inverse reinforcement learning; Inverse optimal control; Deep learning; Reinforcement learning; Machine learning

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APA (6^th Edition):

Zaytsev, A. (2017). Faster apprenticeship learning through inverse optimal control. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99228

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):

Zaytsev, Andrey. “Faster apprenticeship learning through inverse optimal control.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed March 07, 2021. http://hdl.handle.net/2142/99228.

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

MLA Handbook (7^th Edition):

Zaytsev, Andrey. “Faster apprenticeship learning through inverse optimal control.” 2017. Web. 07 Mar 2021.

Vancouver:

Zaytsev A. Faster apprenticeship learning through inverse optimal control. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2142/99228.

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

Council of Science Editors:

Zaytsev A. Faster apprenticeship learning through inverse optimal control. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99228

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

NSYSU

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

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

MLA Handbook (7^th Edition):

Tseng, Yi-Chia. “An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations.” 2015. Web. 07 Mar 2021.

Vancouver:

Tseng Y. An Unified Approach to Inverse Reinforcement Learning by Oppositive Demonstrations. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727115-130716.

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

Council of Science Editors:

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

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

NSYSU

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

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

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

MLA Handbook (7^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Web. 07 Mar 2021.

Vancouver:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

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

Council of Science Editors:

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

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

Delft University of Technology

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

Degree: 2016, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98

►

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

▼

New flexible teaching methods for robotics are needed to automate repetitive tasks that are currently still done by humans. For limited batch sizes, it is too expensive to teach a robot a new task (Smith & Anderson, 2014). Ideally, such flexible robots can be taught a new task by a non-expert. A non-expert is a person who knows the task the robot should perform, but does not have experience in programming a robot. A powerful method that would allow for flexible robotics without the use of an expert is inverse reinforcement learning (IRL). IRL aims to learn the cost function out of demonstrations, this cost function is subsequently used to learn a policy which realizes the desired task. Current implementations focus more on the IRL algorithm itself and assume that there are enough demonstrations available and the quality of these demonstrations is also close enough to the optimal behaviour (Doerr et al., 2015). Whilst actually these demonstrations are very expensive and non-optimal. This thesis focuses on the effect of the quality of input demonstrations on the performance of the learned trajectory. Furthermore, how imperfect demonstrations still can be used, without lowering the performance of the learned trajectory. The first hypothesis is that the performance of the resulting trajectory depends on the average performance of the input demonstrations and the quantity of the input demonstrations has less of an effect. The second hypothesis is that by adding a ranking to the demonstrations, created through the preferences of non-robotic experts, the performance of the learned trajectory would be better than the average performance of the input demonstrations. The preferences of the non-robotic expert are collected through a crowdsourcing experiment. The preferences of the non-robotic expert are used to create an overall performance measurement. This overall performance measurement is used to obtain the sequentially order of the input demonstrations but also to evaluate the final learned trajectories. The results validate the first hypothesis. The average performance of the input demonstrations is determining the performance of the learned trajectory. The second hypothesis could not be confirmed. The results did not show any improvements in the performance of the learned trajectory when the ranking based on the preference of a non-robotic expert is added. It could be argued that the input demonstrations were too similar or the cost features used in IRL are not specific enough to create different cost functions and therefore create differently performing trajectories.

Mechanical, Maritime and Materials Engineering

Biomechanical Engineering

Advisors/Committee Members: Kober, J. (mentor).

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

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

van der Wijden, R (author). “Preference-driven demonstrations ranking for inverse reinforcement learning.” 2016. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98.

MLA Handbook (7^th Edition):

van der Wijden, R (author). “Preference-driven demonstrations ranking for inverse reinforcement learning.” 2016. Web. 07 Mar 2021.

Vancouver:

van der Wijden R(. Preference-driven demonstrations ranking for inverse reinforcement learning. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:4a85d32d-79da-4983-97d7-530c7bb1da98.

Council of Science Editors:

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

Tampere University

7. Dewundara Liyanage, Ishira Uthkarshini. Reward Learning from Demonstrations for Autonomous Earthmoving .

Degree: 2020, Tampere University

URL: https://trepo.tuni.fi/handle/10024/123546

► With the increasing complexity of specific tasks, automation engineers look at various machine learning methods as opposed to methods that require laborious task specifications. Imitation… (more)

▼ With the increasing complexity of specific tasks, automation engineers look at various machine learning methods as opposed to methods that require laborious task specifications. Imitation learning methods have had varying degrees of success in the past. The main drawback of imitation learning methods is their inability to adapt to newer and ever-changing problems which hinder their flexibility. Reinforcement learning aims to solve the problem by learning instead based on a rewarding mechanism. However, a reward function needs to be determined prior to carrying out reinforcement learning. A range of methods have been used to define the reward functions, which are collectively referred to as inverse reinforcement learning methods. The objective of this research is to find a reward function for the autonomous earthmoving of a GIM Machine. In this study, different inverse reinforcement learning implementations were explored. Unsupervised perceptual rewards was selected considering that it is a sample efficient method that is easy to implement on a machine without good simulations for the environment and its interactions. Based on this method, the task is broken down into stages. Demonstration data and stage labels are used to train a stage classifier. When an observation is made, it is classified into one of the stages, and the reward is calculated as a function of the distance to the next stage. Unsupervised perceptual rewards is first used to obtain the reward function for the OpenAI Gym mountain car problem. Then q-learning is used to confirm that reinforcement learning can be applied effectively using the reward function obtained using this unsupervised perceptual rewards method. The method is then applied to demonstrations of the GIM Machine. Both low-level sensor data, as well as image features, have been used to calculate the reward. This research confirms the feasibility of using unsupervised perceptual rewards for reward function calculation and tests its robustness to changes in weather and lighting.

Subjects/Keywords: inverse reinforcement learning ; reinforcement learning ; automation ; reward funtion ; unsupervised perceptual rewards

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

APA (6^th Edition):

Dewundara Liyanage, I. U. (2020). Reward Learning from Demonstrations for Autonomous Earthmoving . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/123546

Chicago Manual of Style (16^th Edition):

Dewundara Liyanage, Ishira Uthkarshini. “Reward Learning from Demonstrations for Autonomous Earthmoving .” 2020. Masters Thesis, Tampere University. Accessed March 07, 2021. https://trepo.tuni.fi/handle/10024/123546.

MLA Handbook (7^th Edition):

Dewundara Liyanage, Ishira Uthkarshini. “Reward Learning from Demonstrations for Autonomous Earthmoving .” 2020. Web. 07 Mar 2021.

Vancouver:

Dewundara Liyanage IU. Reward Learning from Demonstrations for Autonomous Earthmoving . [Internet] [Masters thesis]. Tampere University; 2020. [cited 2021 Mar 07]. Available from: https://trepo.tuni.fi/handle/10024/123546.

Council of Science Editors:

Dewundara Liyanage IU. Reward Learning from Demonstrations for Autonomous Earthmoving . [Masters Thesis]. Tampere University; 2020. Available from: https://trepo.tuni.fi/handle/10024/123546

University of Illinois – Chicago

8. Tirinzoni, Andrea. Adversarial Inverse Reinforcement Learning with Changing Dynamics.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/22081

► Most work on inverse reinforcement learning, the problem of recovering the unknown reward function being optimized by a decision-making agent, has focused on cases where… (more)

Subjects/Keywords: Machine Learning; Inverse Reinforcement Learning; Reinforcement Learning; Adversarial Prediction; Markov Decision Process; Imitation Learning

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

APA (6^th Edition):

Tirinzoni, A. (2017). Adversarial Inverse Reinforcement Learning with Changing Dynamics. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22081

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

Chicago Manual of Style (16^th Edition):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Thesis, University of Illinois – Chicago. Accessed March 07, 2021. http://hdl.handle.net/10027/22081.

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

MLA Handbook (7^th Edition):

Tirinzoni, Andrea. “Adversarial Inverse Reinforcement Learning with Changing Dynamics.” 2017. Web. 07 Mar 2021.

Vancouver:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10027/22081.

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

Council of Science Editors:

Tirinzoni A. Adversarial Inverse Reinforcement Learning with Changing Dynamics. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22081

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

NSYSU

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

Degree: Master, Electrical Engineering, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Cheng, Tien-yu. “Inverse Reinforcement Learning based on Critical State.” 2014. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500.

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

MLA Handbook (7^th Edition):

Cheng, Tien-yu. “Inverse Reinforcement Learning based on Critical State.” 2014. Web. 07 Mar 2021.

Vancouver:

Cheng T. Inverse Reinforcement Learning based on Critical State. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1028114-170500.

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

Council of Science Editors:

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

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

University of Illinois – Chicago

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

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/21987

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Thesis, University of Illinois – Chicago. Accessed March 07, 2021. http://hdl.handle.net/10027/21987.

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

MLA Handbook (7^th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Web. 07 Mar 2021.

Vancouver:

Chen X. Robust Structured Prediction for Process Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10027/21987.

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

Council of Science Editors:

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

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

University of Southern California

11. Kalakrishnan, Mrinal. Learning objective functions for autonomous motion generation.

Degree: PhD, Computer Science, 2014, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3787

► Planning and optimization methods have been widely applied to the problem of trajectory generation for autonomous robotics. The performance of such methods, however, is critically… (more)

▼ Planning and optimization methods have been widely applied to the problem of trajectory generation for autonomous robotics. The performance of such methods, however, is critically dependent on the choice of objective function being optimized, and is non‐trivial to design. On the other end of the spectrum, efforts on learning autonomous behavior from user‐provided demonstrations have largely been focused on reproducing behavior similar in appearance to the provided demonstrations. An alternative approach, known as Inverse Reinforcement Learning (IRL), is to learn an objective function that the demonstrations are assumed to be optimal under. With the help of a planner or trajectory optimizer, such an approach allows the system to synthesize novel behavior in situations that were not experienced in the demonstrations. ❧ We present novel algorithms for IRL that have successfully been applied in two real‐world, competitive robotics settings: (1) In the domain of rough terrain quadruped locomotion, we present an algorithm that learns an objective function for foothold selection based on ”terrain templates”. The learner automatically generates and selects the appropriate features which form the objective function, which reduces the need for feature engineering while attaining a high level of generalization. (2) For the domain of autonomous manipulation, we present a probabilistic model of optimal trajectories, which results in new algorithms for inverse reinforcement learning and trajectory optimization in high‐dimensional settings. We apply this method to two problems in robotic manipulation: redundancy resolution in inverse kinematics, and trajectory optimization for grasping and manipulation. Both methods have proven themselves as part of larger integrated systems in competitive settings against other teams, where testing was conducted by an independent test team in situations that were not seen during training. Advisors/Committee Members: Schaal, Stefan (Committee Chair), Sukhatme, Gaurav S. (Committee Member), Valero-Cuevas, Francisco (Committee Member).

Subjects/Keywords: robotics; machine learning; motion planning; trajectory optimization; inverse reinforcement learning; reinforcement learning; locomotion; manipulation

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

APA (6^th Edition):

Kalakrishnan, M. (2014). Learning objective functions for autonomous motion generation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3787

Chicago Manual of Style (16^th Edition):

Kalakrishnan, Mrinal. “Learning objective functions for autonomous motion generation.” 2014. Doctoral Dissertation, University of Southern California. Accessed March 07, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3787.

MLA Handbook (7^th Edition):

Kalakrishnan, Mrinal. “Learning objective functions for autonomous motion generation.” 2014. Web. 07 Mar 2021.

Vancouver:

Kalakrishnan M. Learning objective functions for autonomous motion generation. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2021 Mar 07]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3787.

Council of Science Editors:

Kalakrishnan M. Learning objective functions for autonomous motion generation. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/369146/rec/3787

University of New South Wales

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

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

URL: http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true

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

▼ Apprenticeship Learning (AL) uses data collected from humans on tasks to design machine-learning algorithms to imitate the skills used by humans. Such a powerful approach to developing autonomous machines comes with challenges stemming from its reliance on the existence of expert humans who can perform the task and their willingness to be available. In this thesis, Apprenticeship Bootstrapping (ABS) is proposed as a new learning algorithm that relies on humans who are experts on less-complex tasks to aggregate the skills required for more complex ones. ABS has been validated using a ground and aerial coordination task (GACT), where an unmanned aerial vehicle (UAV) needs to autonomously follow a group of unmanned ground vehicles (UGVs) while maintaining the group within the field of view of the UAV's camera.ABS decomposes the complex control task for the UAV operator into simpler sub-tasks which an operator is available to perform and to collect demonstrations from. Two proposed learning approaches, apprenticeship bootstrapping via deep learning and inverse reinforcement learning (ABS-DL and ABS-IRL, respectively), are used to address this challenge. In ABS-DL, deep learning, which has shown dramatic advances in terms of effectively work on raw data, is attractive for learning directly from demonstrations of sub-tasks. However, it may not be effective for acquiring a desirable policy, as it relies heavily on the quality of demonstrations. The quality of demonstrations may not be good because of errors in collecting expert demonstrations caused by the limitations of the techniques, or poor demonstrations caused by the expert. ABS-IRL addresses these issues by recovering the expert reward function from demonstrations on the sub-tasks. In ABS-IRL, during every learning episode, the learner acts learned policy and re-optimizes its learned model while performing the task. This learning approach allows the leaner to avoid learning incorrect or poor demonstrations.Both ABS-DL and ABS-IRL were tested on GACT and results confirmed that ABS-IRL could bootstrap the more complex skill from lower less-complex ones. Advisors/Committee Members: Abbass, Hussein, Engineering & Information Technology, UNSW Canberra, UNSW, Garratt, Mathew, Engineering & Information Technology, UNSW Canberra, UNSW, Bui, Lam, Le Quy Don - Viet nam.

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

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Nguyen, Hung. “Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles.” 2018. Masters Thesis, University of New South Wales. Accessed March 07, 2021. http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Nguyen, Hung. “Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles.” 2018. Web. 07 Mar 2021.

Vancouver:

Nguyen H. Apprenticeship Bootstrapping: Multi-Skill Reinforcement Learning for Autonomous Unmanned Aerial Vehicles. [Internet] [Masters thesis]. University of New South Wales; 2018. [cited 2021 Mar 07]. Available from: http://handle.unsw.edu.au/1959.4/60412 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52104/SOURCE02?view=true.

Council of Science Editors:

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

NSYSU

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

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Chiang, Hsuan-yi. “Action Segmentation and Learning by Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230.

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

MLA Handbook (7^th Edition):

Chiang, Hsuan-yi. “Action Segmentation and Learning by Inverse Reinforcement Learning.” 2015. Web. 07 Mar 2021.

Vancouver:

Chiang H. Action Segmentation and Learning by Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906115-151230.

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

Council of Science Editors:

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

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

University of Georgia

14. Bogert, Kenneth Daniel. Inverse reinforcement learning for robotic applications.

Degree: 2017, University of Georgia

URL: http://hdl.handle.net/10724/36625

► Robots deployed into many real-world scenarios are expected to face situations that their designers could not anticipate. Machine learning is an effective tool for extending… (more)

Subjects/Keywords: robotics; inverse reinforcement learning; machine learning; Markov decision process

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APA (6^th Edition):

Bogert, K. D. (2017). Inverse reinforcement learning for robotic applications. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/36625

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):

Bogert, Kenneth Daniel. “Inverse reinforcement learning for robotic applications.” 2017. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/36625.

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

MLA Handbook (7^th Edition):

Bogert, Kenneth Daniel. “Inverse reinforcement learning for robotic applications.” 2017. Web. 07 Mar 2021.

Vancouver:

Bogert KD. Inverse reinforcement learning for robotic applications. [Internet] [Thesis]. University of Georgia; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/36625.

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

Council of Science Editors:

Bogert KD. Inverse reinforcement learning for robotic applications. [Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/36625

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

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

Degree: PhD, 2000, University of Sussex

URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189

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 (6^th 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 (16^th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Doctoral Dissertation, University of Sussex. Accessed March 07, 2021. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324189.

MLA Handbook (7^th Edition):

Paraskevopoulos, Vasileios. “Design of optimal neural network control strategies with minimal a priori knowledge.” 2000. Web. 07 Mar 2021.

Vancouver:

Paraskevopoulos V. Design of optimal neural network control strategies with minimal a priori knowledge. [Internet] [Doctoral dissertation]. University of Sussex; 2000. [cited 2021 Mar 07]. 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

University of Georgia

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

Degree: 2017, University of Georgia

URL: http://hdl.handle.net/10724/36698

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

Subjects/Keywords: Inverse Reinforcement Learning; One Switch Utility Functions; Entropy; Apprenticeship Learning; Markov Decision Process

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

APA (6^th Edition):

Das, I. (2017). Inverse reinforcement learning of risk-sensitive utility. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/36698

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):

Das, Indrajit. “Inverse reinforcement learning of risk-sensitive utility.” 2017. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/36698.

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

MLA Handbook (7^th Edition):

Das, Indrajit. “Inverse reinforcement learning of risk-sensitive utility.” 2017. Web. 07 Mar 2021.

Vancouver:

Das I. Inverse reinforcement learning of risk-sensitive utility. [Internet] [Thesis]. University of Georgia; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/36698.

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

Council of Science Editors:

Das I. Inverse reinforcement learning of risk-sensitive utility. [Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/36698

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

University of Georgia

17. Jain, Vinamra. Maximum likelihood approach for model-free inverse reinforcement learning.

Degree: 2018, University of Georgia

URL: http://hdl.handle.net/10724/37796

► Preparing an intelligent system in advance to respond optimally in every possible situation is difficult. Machine learning approaches like Inverse Reinforcement Learning can help learning… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Maximum Likelihood Estimation; Markov Decision Process; Learning from Demonstrations

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

APA (6^th Edition):

Jain, V. (2018). Maximum likelihood approach for model-free inverse reinforcement learning. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37796

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):

Jain, Vinamra. “Maximum likelihood approach for model-free inverse reinforcement learning.” 2018. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/37796.

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

MLA Handbook (7^th Edition):

Jain, Vinamra. “Maximum likelihood approach for model-free inverse reinforcement learning.” 2018. Web. 07 Mar 2021.

Vancouver:

Jain V. Maximum likelihood approach for model-free inverse reinforcement learning. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/37796.

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

Council of Science Editors:

Jain V. Maximum likelihood approach for model-free inverse reinforcement learning. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37796

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

NSYSU

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

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

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Chen, Yan-heng. “Analysis of Another Left Shift Binary GCD Algorithm.” 2009. Thesis, NSYSU. Accessed March 07, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741.

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

MLA Handbook (7^th Edition):

Chen, Yan-heng. “Analysis of Another Left Shift Binary GCD Algorithm.” 2009. Web. 07 Mar 2021.

Vancouver:

Chen Y. Analysis of Another Left Shift Binary GCD Algorithm. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Mar 07]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714109-121741.

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

Council of Science Editors:

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

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

University of Oxford

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

Degree: PhD, 2018, University of Oxford

URL: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819

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

▼ In order to enable more widespread application of robots, we are required to reduce the human effort for the introduction of existing robotic platforms to new environments and tasks. In this thesis, we identify three complementary strategies to address this challenge, via the use of imitation learning, domain adaptation, and transfer learning based on simulations. The overall work strives to reduce the effort of generating training data by employing inexpensively obtainable labels and by transferring information between different domains with deviating underlying properties. Imitation learning enables a straightforward way for untrained personnel to teach robots to perform tasks by providing demonstrations, which represent a comparably inexpensive source of supervision. We develop a scalable approach to identify the preferences underlying demonstration data via the framework of inverse reinforcement learning. The method enables integration of the extracted preferences as cost maps into existing motion planning systems. We further incorporate prior domain knowledge and demonstrate that the approach outperforms the baselines including manually crafted cost functions. In addition to employing low-cost labels from demonstration, we investigate the adaptation of models to domains without available supervisory information. Specifically, the challenge of appearance changes in outdoor robotics such as illumination and weather shifts is addressed using an adversarial domain adaptation approach. A principal advantage of the method over prior work is the straightforwardness of adapting arbitrary, state-of-the-art neural network architectures. Finally, we demonstrate performance benefits of the method for semantic segmentation of drivable terrain. Our last contribution focuses on simulation to real world transfer learning, where the characteristic differences are not only regarding the visual appearance but the underlying system dynamics. Our work aims at parallel training in both systems and mutual guidance via auxiliary alignment rewards to accelerate training for real world systems. The approach is shown to outperform various baselines as well as a unilateral alignment variant.

Subjects/Keywords: 006.3; Machine learning; Robotics; Domain Adaptation; Imitation Learning; Inverse Reinforcement Learning; Mobile Robotics; Transfer Learning; Autonomous Driving

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Doctoral Dissertation, University of Oxford. Accessed March 07, 2021. http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

MLA Handbook (7^th Edition):

Wulfmeier, Markus. “Efficient supervision for robot learning via imitation, simulation, and adaptation.” 2018. Web. 07 Mar 2021.

Vancouver:

Wulfmeier M. Efficient supervision for robot learning via imitation, simulation, and adaptation. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2021 Mar 07]. Available from: http://ora.ox.ac.uk/objects/uuid:2b5eeb55-639a-40ae-83b7-bd01fc8fd6cc ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757819.

Council of Science Editors:

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

University of Plymouth

20. Loviken, Pontus. Fast online model learning for controlling complex real-world robots.

Degree: PhD, 2019, University of Plymouth

URL: http://hdl.handle.net/10026.1/15078

► How can real robots with many degrees of freedom - without previous knowledge of themselves or their environment - act and use the resulting observations… (more)

▼ How can real robots with many degrees of freedom - without previous knowledge of themselves or their environment - act and use the resulting observations to efficiently develop the ability to generate a wide set of useful behaviours? This thesis presents a novel framework that enables physical robots with many degrees of freedom to rapidly learn models for control from scratch. This can be done in previously inaccessible problem domains characterised by a lack of direct mappings from motor actions to outcomes, as well as state and action spaces too large for the full forward dynamics to be learned and used explicitly. The proposed framework is able to cope with these issues by the use of a set of local Goal Babbling models, that maps every outcome in a low dimensional task space to a specific action, together with a sparse higher level Reinforcement Learning model, that learns to navigate between the contexts from which each Goal Babbling model can be used. The two types of models can then be learned online an in parallel, using only the data a robot can collect by interacting with its environment. To show the potential of the approach we present two possible implementations of the framework, over two separate robot platforms: a simulated planar arm with up to 1, 000 degrees of freedom, and a real humanoid robot with 25 degrees of freedom. The results show that learning is rapid and essentially unaffected by the number of degrees of freedom of the robot, allowing for the generation of complex behaviours and skills after a relatively short training time. The planar arm is able to strategically plan series of motions in order to move its end-effector between any two parts of a crowded environment, within 10, 000 iterations. The humanoid robot is able to freely transition between states such as lying on the back, belly, and sides, and occasionally also sitting up, within only 1, 000 iterations. This corresponds to 30 − 60 minutes of real-world interactions. The main contribution of this thesis is to provide a framework for solving a control learning problem, previously largely unexplored with no obvious solutions, but with strong analogies to, for example, early learning of body orientation control in infants. This thesis examined two quite different implementations of the proposed framework, and showed success in both cases for two different control learning problem.

Subjects/Keywords: model learning; Reinforcement learning; Online learning; Goal babbling; inverse models; Micro data learning; Developmental robotics; real-world robots; sensorimotor control

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

APA (6^th Edition):

Loviken, P. (2019). Fast online model learning for controlling complex real-world robots. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/15078

Chicago Manual of Style (16^th Edition):

Loviken, Pontus. “Fast online model learning for controlling complex real-world robots.” 2019. Doctoral Dissertation, University of Plymouth. Accessed March 07, 2021. http://hdl.handle.net/10026.1/15078.

MLA Handbook (7^th Edition):

Loviken, Pontus. “Fast online model learning for controlling complex real-world robots.” 2019. Web. 07 Mar 2021.

Vancouver:

Loviken P. Fast online model learning for controlling complex real-world robots. [Internet] [Doctoral dissertation]. University of Plymouth; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10026.1/15078.

Council of Science Editors:

Loviken P. Fast online model learning for controlling complex real-world robots. [Doctoral Dissertation]. University of Plymouth; 2019. Available from: http://hdl.handle.net/10026.1/15078

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

Degree: Docteur es, Informatique, 2012, Avignon

URL: http://www.theses.fr/2012AVIG0185

►

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

▼

Les récents progrès dans le domaine du traitement du langage ont apporté un intérêt significatif à la mise en oeuvre de systèmes de dialogue parlé. Ces derniers sont des interfaces utilisant le langage naturel comme medium d'interaction entre le système et l'utilisateur. Le module de gestion de dialogue choisit le moment auquel l'information qu'il choisit doit être échangée avec l'utilisateur. Ces dernières années, l'optimisation de dialogue parlé en utilisant l'apprentissage par renforcement est devenue la référence. Cependant, une grande partie des algorithmes utilisés nécessite une importante quantité de données pour être efficace. Pour gérer ce problème, des simulations d'utilisateurs ont été introduites. Cependant, ces modèles introduisent des erreurs. Par un choix judicieux d'algorithmes, la quantité de données d'entraînement peut être réduite et ainsi la modélisation de l'utilisateur évitée. Ces travaux concernent une partie des contributions présentées. L'autre partie des travaux consiste à proposer une modélisation à partir de données réelles des utilisateurs au moyen de l'apprentissage par renforcement inverse

Recent advancements in the area of spoken language processing and the wide acceptance of portable devices, have attracted signicant interest in spoken dialogue systems.These conversational systems are man-machine interfaces which use natural language (speech) as the medium of interaction.In order to conduct dialogues, computers must have the ability to decide when and what information has to be exchanged with the users. The dialogue management module is responsible to make these decisions so that the intended task (such as ticket booking or appointment scheduling) can be achieved.Thus learning a good strategy for dialogue management is a critical task.In recent years reinforcement learning-based dialogue management optimization has evolved to be the state-of-the-art. A majority of the algorithms used for this purpose needs vast amounts of training data.However, data generation in the dialogue domain is an expensive and time consuming process. In order to cope with this and also to evaluatethe learnt dialogue strategies, user modelling in dialogue systems was introduced. These models simulate real users in order to generate synthetic data.Being computational models, they introduce some degree of modelling errors. In spite of this, system designers are forced to employ user models due to the data requirement of conventional reinforcement learning algorithms can learn optimal dialogue strategies from limited amount of training data when compared to the conventional algorithms. As a consequence of this, user models are no longer required for the purpose of optimization, yet they continue to provide a fast and easy means for quantifying the quality of dialogue strategies. Since existing methods for user modelling are relatively less realistic compared to real user behaviors, the focus is shifted towards user modelling by means of inverse reinforcement learning. Using experimental results, the proposed…

Advisors/Committee Members: Lefèvre, Fabrice (thesis director), Pietquin, Olivier (thesis director).

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

Record Details Similar Records Cite « Share

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

APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Chandramohan, Senthilkumar. “Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?.” 2012. Doctoral Dissertation, Avignon. Accessed March 07, 2021. http://www.theses.fr/2012AVIG0185.

MLA Handbook (7^th Edition):

Chandramohan, Senthilkumar. “Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?.” 2012. Web. 07 Mar 2021.

Vancouver:

Chandramohan S. Revisiting user simulation in dialogue systems : do we still need them ? : will imitation play the role of simulation ? : Revisiter la simulation d'utilisateurs dans les systèmes de dialogue parlé : est-elle encore nécessaire ? : est-ce que l'imitation peut jouer le rôle de la simulation ?. [Internet] [Doctoral dissertation]. Avignon; 2012. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2012AVIG0185.

Council of Science Editors:

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

University of Georgia

22. Bhat, Sanath Govinda. Learning driver preferences for freeway merging using multitask irl.

Degree: 2018, University of Georgia

URL: http://hdl.handle.net/10724/37116

► Most automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Hierarchical Bayesian Model; Multitask; Highway Merging; NGSIM; Likelihood Weighting

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APA (6^th Edition):

Bhat, S. G. (2018). Learning driver preferences for freeway merging using multitask irl. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37116

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):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2018. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/37116.

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

MLA Handbook (7^th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2018. Web. 07 Mar 2021.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/37116.

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

Council of Science Editors:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37116

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

University of Georgia

23. Bhat, Sanath Govinda. Learning driver preferences for freeway merging using multitask irl.

Degree: 2018, University of Georgia

URL: http://hdl.handle.net/10724/37273

► Most automobile manufacturers today have invested heavily in the research and design of implementing autonomy in their cars. One important and challenging problem faced by… (more)

Subjects/Keywords: Inverse Reinforcement Learning; Hierarchical Bayesian Model; Multitask; Highway Merging; NGSIM; Likelihood Weighting

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

APA (6^th Edition):

Bhat, S. G. (2018). Learning driver preferences for freeway merging using multitask irl. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37273

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):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2018. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/37273.

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

MLA Handbook (7^th Edition):

Bhat, Sanath Govinda. “Learning driver preferences for freeway merging using multitask irl.” 2018. Web. 07 Mar 2021.

Vancouver:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/37273.

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

Council of Science Editors:

Bhat SG. Learning driver preferences for freeway merging using multitask irl. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37273

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

Virginia Tech

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

Degree: MS, Computer Science, 2003, Virginia Tech

URL: http://hdl.handle.net/10919/34728

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

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

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APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Shiraev, Dmitry Eric. “Inverse Reinforcement Learning and Routing Metric Discovery.” 2003. Masters Thesis, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/34728.

MLA Handbook (7^th Edition):

Shiraev, Dmitry Eric. “Inverse Reinforcement Learning and Routing Metric Discovery.” 2003. Web. 07 Mar 2021.

Vancouver:

Shiraev DE. Inverse Reinforcement Learning and Routing Metric Discovery. [Internet] [Masters thesis]. Virginia Tech; 2003. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/34728.

Council of Science Editors:

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

Wright State University

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

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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305

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

▼ According to a Pew Research study, 41% of Americans have personally experienced online harassment and two-thirds of Americans have witnessed harassment in 2017. Hence, online harassment detection is vital for securing and sustaining the popularity and viability of online social networks. Machine learning techniques play a crucial role in automatic harassment detection. One of the challenges of using supervised approaches is training data imbalance. Existing text generation techniques can help augment the training data, but they are still inadequate and ineffective. This research explores the role of domain-specific knowledge to complement the limited training data available for training a text generator.We conduct domain-specific text generation by combining inverse reinforcement learning (IRL) with domain-specific knowledge. Our approach includes two adversarial nets, a text generator and a Reward Approximator (RA). The objective of the text generator is to generate domain-specific text that is hard to discriminate from the real-world domain-specific text. The objective of the reward approximator is to discriminate the generated domain-specific text from the real-world text. During adversarial training, the generator and the RA play a mini-max game and try to arrive at a win-win state. Ultimately, augmenting diversified and semantically meaningful, generated domain-specific data to the existing dataset improves detection of domain-specific text. In addition to developing the Generative Adversarial Network-based framework, we also present a novel evaluation that uses variants of the BLEU metric to measure the diversity of generated text; uses perplexity and cosine similarity to measure the quality of the generated text. Experimental results show that the proposed framework outperforms a previous baseline (IRL without domain knowledge) on harassment (i.e., Abusive and Hate speech) tweet generation. Additionally, the generated tweets effectively augment the training data for online abusive and hate speech detection (tweet classification) resulting in a 9% accuracy improvement in classification using the augmented training set compared to the existing training set. Advisors/Committee Members: Sheth, Amit (Advisor).

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

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APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Nalamothu, Abhishek. “Abusive and Hate Speech Tweets Detection with Text Generation.” 2019. Masters Thesis, Wright State University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305.

MLA Handbook (7^th Edition):

Nalamothu, Abhishek. “Abusive and Hate Speech Tweets Detection with Text Generation.” 2019. Web. 07 Mar 2021.

Vancouver:

Nalamothu A. Abusive and Hate Speech Tweets Detection with Text Generation. [Internet] [Masters thesis]. Wright State University; 2019. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1567510940365305.

Council of Science Editors:

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

University of Pennsylvania

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

Degree: 2019, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/3509

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

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

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

APA (6^th Edition):

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

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

Chicago Manual of Style (16^th Edition):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Thesis, University of Pennsylvania. Accessed March 07, 2021. https://repository.upenn.edu/edissertations/3509.

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

MLA Handbook (7^th Edition):

Wen, Min. “Reinforcement Learning With High-Level Task Specifications.” 2019. Web. 07 Mar 2021.

Vancouver:

Wen M. Reinforcement Learning With High-Level Task Specifications. [Internet] [Thesis]. University of Pennsylvania; 2019. [cited 2021 Mar 07]. Available from: https://repository.upenn.edu/edissertations/3509.

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

Council of Science Editors:

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

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

Oregon State University

27. Proper, Scott. Scaling multiagent reinforcement learning.

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

URL: http://hdl.handle.net/1957/13662

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

▼ Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products using multiple vehicles with stochastic demands, a multiagent predator-prey domain, and a domain based on a real-time strategy game. To mitigate the problem of state-space explosion, this thesis present several approaches that mitigate each of these curses. "Tabular linear functions" (TLFs) are introduced that generalize tile-coding and linear value functions and allow learning of complex nonlinear functions in high-dimensional state-spaces. It is also shown how to adapt TLFs to relational domains, creating a "lifted" version called relational templates. To mitigate the problem of action-space explosion, the replacement of complete joint action space search with a form of hill climbing is described. To mitigate the problem of outcome space explosion, a more efficient calculation of the expected value of the next state is shown, and two real-time dynamic programming algorithms based on afterstates, ASH-learning and ATR-learning, are introduced. Lastly, two approaches that scale by treating a multiagent domain as being formed of several coordinating agents are presented. "Multiagent H-learning" and "Multiagent ASH-learning" are described, where coordination is achieved through a method called "serial coordination". This technique has the benefit of addressing each of the three curses of dimensionality simultaneously by reducing the space of states and actions each local agent must consider. The second approach to multiagent coordination presented is "assignment-based decomposition", which divides the action selection step into an assignment phase and a primitive action selection step. Like the multiagent approach, assignment-based decomposition addresses all three curses of dimensionality simultaneously by reducing the space of states and actions each group of agents must consider. This method is capable of much more sophisticated coordination. Experimental results are presented which show successful application of all methods described. These results demonstrate that the scaling techniques described in this thesis can greatly mitigate the three curses of dimensionality and allow solutions for multiagent domains to scale to large numbers of agents, and complex state and outcome spaces. Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).

Subjects/Keywords: Reinforcement learning; Reinforcement learning

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APA (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed March 07, 2021. http://hdl.handle.net/1957/13662.

MLA Handbook (7^th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 07 Mar 2021.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1957/13662.

Council of Science Editors:

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

Oregon State University

28. Mehta, Neville. Hierarchical structure discovery and transfer in sequential decision problems.

Degree: PhD, Computer Science, 2011, Oregon State University

URL: http://hdl.handle.net/1957/25199

► Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or… (more)

▼ Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first contribution is the introduction of two frameworks for the transfer of hierarchical structure in sequential decision problems. The MASH framework facilitates transfer among multiple agents coordinating within a domain. The VRHRL framework allows an agent to transfer its knowledge across a family of domains that share the same transition dynamics but have differing reward dynamics. Both MASH and VRHRL are validated empirically in large domains and the results demonstrate significant speedup in the solutions due to transfer. The second contribution is a new approach to the discovery of hierarchical structure in sequential decision problems. HI-MAT leverages action models to analyze the relevant dependencies in a hierarchically-generated trajectory and it discovers hierarchical structure that transfers to all problems whose actions share the same relevant dependencies as the single source problem. HierGen advances HI-MAT by learning simple action models, leveraging these models to analyze non-hierarchically-generated trajectories from multiple source problems in a robust causal fashion, and discovering hierarchical structure that transfers to all problems whose actions share the same causal dependencies as those in the source problems. Empirical evaluations in multiple domains demonstrate that the discovered hierarchical structures are comparable to manually-designed structures in quality and performance. Action models are essential to hierarchical structure discovery and other aspects of intelligent behavior. The third contribution of this dissertation is the introduction of two general frameworks for learning action models in sequential decision problems. In the MBP framework, learning is user-driven; in the PLEX framework, the learner generates its own problems. The frameworks are formally analyzed and reduced to concept learning with one-sided error. A general action-modeling language is shown to be efficiently learnable in both frameworks. Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).

Subjects/Keywords: hierarchical reinforcement learning; Reinforcement learning

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

APA (6^th Edition):

Mehta, N. (2011). Hierarchical structure discovery and transfer in sequential decision problems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25199

Chicago Manual of Style (16^th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Doctoral Dissertation, Oregon State University. Accessed March 07, 2021. http://hdl.handle.net/1957/25199.

MLA Handbook (7^th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 07 Mar 2021.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1957/25199.

Council of Science Editors:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25199

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

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

URL: http://hdl.handle.net/20.500.12380/300894

► 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 (6^th 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 (16^th Edition):

Heikkilä, Filip. “Autonomous Mapping of Unknown Environments Using a UAV .” 2020. Thesis, Chalmers University of Technology. Accessed March 07, 2021. 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 (7^th Edition):

Heikkilä, Filip. “Autonomous Mapping of Unknown Environments Using a UAV .” 2020. Web. 07 Mar 2021.

Vancouver:

Heikkilä F. Autonomous Mapping of Unknown Environments Using a UAV . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Mar 07]. 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

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

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

URL: http://hdl.handle.net/2152/25916

► 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…

10 more images…

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

APA (6^th 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 (16^th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Masters Thesis, University of Texas – Austin. Accessed March 07, 2021. http://hdl.handle.net/2152/25916.

MLA Handbook (7^th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Web. 07 Mar 2021.

Vancouver:

Zhang R. Action selection in modular reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2021 Mar 07]. 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

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Open Access Theses and Dissertations