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Full-Text Articles in Physical Sciences and Mathematics
Convergence Of A Reinforcement Learning Algorithm In Continuous Domains, Stephen Carden
Convergence Of A Reinforcement Learning Algorithm In Continuous Domains, Stephen Carden
All Dissertations
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and actions have been well studied, and there exist algorithms capable of producing a sequence of policies which converge to an optimal policy with probability one. Convergence guarantees for problems with continuous states also exist. Until recently, no online algorithm for continuous states and continuous actions has been proven to produce optimal policies. This Dissertation contains the results of research into reinforcement learning algorithms for problems in which both the state and action spaces are continuous. The problems to be solved are introduced formally as …
A Continuous Learning Strategy For Self-Organizing Maps Based On Convergence Windows, Gregory T. Breard
A Continuous Learning Strategy For Self-Organizing Maps Based On Convergence Windows, Gregory T. Breard
Senior Honors Projects
A self-organizing map (SOM) is a type of artificial neural network that has applications in a variety of fields and disciplines. The SOM algorithm uses unsupervised learning to produce a low-dimensional representation of high- dimensional data. This is done by 'fitting' a grid of nodes to a data set over a fixed number of iterations. With each iteration, the nodes of the map are adjusted so that they appear more like the data points. The low-dimensionality of the resulting map means that it can be presented graphically and be more intuitively interpreted by humans. However, it is still essential to …