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Thesis (M.A. Sc.)--University of Toronto, 2006. / Source: Masters Abstracts International, Volume: 44-06, page: 2869. Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains xiv, 121 p. : ill. (some col.), col. map. Includes abstract. Includes bibliographical references (p. 116-121).

Thesis (M.S. in Aeronautical Engineering)--Naval Postgraduate School, September 1990. / Thesis Advisor(s): Collins, Daniel J. Second Reader: Schmidt, Louis V. "September 1990." DTIC Identifier(s): Neural Nets, Flight Control Systems, Adaptive Control Systems, Computer Programs, Parallel Processing, Distributed Data Processing, Theses, Attack Aircraft, Equations of Motion, Control Theory, A-4 Aircraft. Author(s) subject terms: Neural Networks, Adaptive Control, Backpropagation, Parameter Estimation, Parallel Distributed Processing. Includes bibliographical references (p. 41). Also available in print.

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 149-152). Also available in electronic format on the Internet.

Dissertation (PhD)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

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A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. December 14, 2015 / Developing a robot that can operate autonomously is an active area in robotics research. An autonomously operating robot can have a tremendous number of applications such as: surveillance and inspection; search and rescue; and operating in hazardous environments. Reinforcement learning, a branch of machine learning, provides an attractive framework for developing robust control algorithms since it is less demanding in terms of both knowledge and programming effort. Given a reward function, reinforcement learning employs a trial-and-error concept to make an agent learn. It is computationally intractable in practice for an agent to learn “de novo”, thus it is important to provide the learning system with “a priori” knowledge. Such prior knowledge would be in the form of demonstrations performed by the teacher. However, prior knowledge does not necessarily guarantee that the agent will perform well. The performance of the agent usually depends on the reward function, since the reward function describes the formal specification of the control task. However, problems arise with complex reward function that are difficult to specify manually. In order to address these problems, apprenticeship learning via inverse reinforcement learning is used. Apprenticeship learning via inverse reinforcement learning can be used to extract a reward function from the set of demonstrations so that the agent can optimise its performance with respect to that reward function. In this research, a flight controller for the Ar.Drone quadrotor was created using a reinforcement learning algorithm and function approximators with some prior knowledge. The agent was able to perform a manoeuvre that is similar to the one demonstrated by the teacher.