{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/424"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/424","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"Design and control of large collections of learning agents","abstract":"The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The Collective Intelligence project at NASA/Ames provides an elegant, machinelearning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This thesis will focus on creating additional key properties and algorithms within the mathematics of the Framework of Collectives. The additions will allow agents to learn quickly in more complex systems. Also they will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.","abstract_html":"The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The Collective Intelligence project at NASA/Ames provides an elegant, machinelearning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. 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