Reinforcement learning A comparison of learning agents in environments with large discrete state spaces

Examensarbete för kandidatexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
203119.pdfFulltext672.16 kBAdobe PDFView/Open
Type: Examensarbete för kandidatexamen
Bachelor Thesis
Title: Reinforcement learning A comparison of learning agents in environments with large discrete state spaces
Authors: Andersson, Johan
Kristiansson, Emil
Persson, Joakim
Toom, Daniel
Sandberg Eriksson, Adam
Widstam, Joppe
Abstract: For some real-world optimization problems where the best behavior is sought, it is infeasible to search for a solution by making a model of the problem and performing calculations on it. When this is the case, good solutions can sometimes be found by trial and error. Reinforcement learning is a way of finding optimal behavior by systematic trial and error. This thesis aims to compare different reinforcement learning techniques and evaluate them. Model-based interval estimation (MBIE) and Explicit Explore or Exploit using dynamic bayesian networks (DBN-E3) are two algorithms that are evaluated. To evaluate the techniques, learning agents were constructed using the algorithms and then simulated in the environment Invasive Species from the Reinforcement Learning Competition. The results of the study show that an optimized version of DBN-E3 is better than MBIE at finding an optimal or near optimal behavior policy in Invasive Species for a selection of environment parameters. Using a factored model like a DBN shows certain advantages operating in Invasive Species, which is a factored environment. For example it achieves a near optimal policy within fewer episodes than MBIE.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2014
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
Collection:Examensarbeten för kandidatexamen // Bachelor Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.