dc.contributor.authorXiao, Dan
dc.date.accessioned2010-11-30T06:27:03Z
dc.date.accessioned2017-07-23T08:29:11Z
dc.date.available2010-11-30T06:27:03Z
dc.date.available2017-07-23T08:29:11Z
dc.date.copyright2010en_US
dc.date.issued2010
dc.identifier.citationXiao, D. (2010). Self-organizing neural architectures and multi-agent cooperative reinforcement learning. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/42406
dc.description.abstractMulti-agent system, wherein multiple agents work to perform tasks jointly through their interaction, is a fairly well studied problem. Many approaches to multi-agent learning exist, among which, reinforcement learning is widely used, as it does not require an explicit model of the environment. However, limitations remain in current multi-agent reinforcement learning approaches, including adaptability and scalability in complex and specialized multi-agent domains. In any multi-agent reinforcement learning system, two major considerations are the reinforcement learning methods used and the cooperative strategies among agents. In this research work, we propose to adopt a self-organizing neural network model, named Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation (TD-FALCON), for multi-agent reinforcement learning. TD-FALCON performs online and incremental learning in real-time with and without immediate reward signals. It thus enables an agent to learn effectively in a dynamic environment.en_US
dc.format.extent148 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleSelf-organizing neural architectures and multi-agent cooperative reinforcement learningen_US
dc.typeThesis
dc.contributor.researchEmerging Research Laben_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.contributor.supervisorTan Ah Hwee (SCE)en_US
dc.description.degreeDOCTOR OF PHILOSOPHY (SCE)en_US
dc.contributor.organizationSchool of Computer Engineeringen_US


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