Reinforcement learning in agent systems
Abstract
This dissertation pertains to the area of Machine Learning and especially to the subfield of Reinforcement Learning. Reinforcement learning comprises an appealing solution to problems with limited environmental feedback. The reinforcement learning framework provides the appropriate tools for solving complex problems, unlike other machine learning frameworks where correct labeled examples are necessary. For example, it is possible that the environment that an autonomous agent will act, may be unknown. Despite the research efforts and the successes in reinforcement learning, several research topics are still open. The contribution of this thesis is two-fold: a) it concerns the deployment of methods in multiagent systems and b) it uses the reinforcement learning framework to solve complex problems like focused crawling and ensemble pruning. Firstly, the thesis concentrates on the problem of coordinating a group of autonomous agents in order to achieve a common goal. For dealing with this ...
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