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Please use this identifier to cite or link to this item: http://hdl.handle.net/1993/289

Title: Reinforcement learning in biologically-inspired collective robotics: a rough set approach
Authors: Henry, Christopher
Supervisor: Peters, James F. (Electrical and Computer Engineering)
Examining Committee: Gunderson, David S. (Mathematics) Fazel-Rezai, Reza (Electrical and Computer Engineering)
Graduation Date: May 2006
Keywords: Approximation space
Ecosystem
Ethology
Reinforcement Learning
Rough Sets
Swarm
Target Tracking
Issue Date: 19-Sep-2006
Abstract: This thesis presents a rough set approach to reinforcement learning. This is made possible by considering behaviour patterns of learning agents in the context of approximation spaces. Rough set theory introduced by Zdzisław Pawlak in the early 1980s provides a ground for deriving pattern-based rewards within approximation spaces. Learning can be considered episodic. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards at the end of each episode. Reference rewards provide a standard for reinforcement comparison as well as the actor-critic method of reinforcement learning. In addition, approximation spaces provide a basis for deriving episodic weights that provide a basis for a new form of off-policy Monte Carlo learning control method. A number of conventional and pattern-based reinforcement learning methods are investigated in this thesis. In addition, this thesis introduces two learning environments used to compare the algorithms. The first is a Monocular Vision System used to track a moving target. The second is an artificial ecosystem testbed that makes it possible to study swarm behaviour by collections of biologically-inspired bots. The simulated ecosystem has an ethological basis inspired by the work of Niko Tinbergen, who introduced in the 1960s methods of observing and explaining the behaviour of biological organisms that carry over into the study of the behaviour of interacting robotic devices that cooperate to survive and to carry out highly specialized tasks. Agent behaviour during each episode is recorded in a decision table called an ethogram, which records features such as states, proximate causes, responses (actions), action preferences, rewards and decisions (actions chosen and actions rejected). At all times an agent follows a policy that maps perceived states of the environment to actions. The goal of the learning algorithms is to find an optimal policy in a non-stationary environment. The results of the learning experiments with seven forms of reinforcement learning are given. The contribution of this thesis is a comprehensive introduction to a pattern-based evaluation of behaviour during reinforcement learning using approximation spaces.
URI: http://hdl.handle.net/1993/289
Type: Electronic Thesis or Dissertation
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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