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Title: Lying in pursuit evasion task with multi-agent reinforcement learning
Authors: Cheng, Damien Shiao Kiat
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Cheng, D. S. K. (2022). Lying in pursuit evasion task with multi-agent reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: Swarm behaviour in nature has long been an area of research, through which many algorithms have been developed and have found applications in modern problems. A particular research field of multi-agent systems, which are more general to swarms, focuses on using multi-agent reinforcement learning to develop and learn policies of high performance. Communication between agents do exist within swarms and within multi-agent systems, and have been modelled during research. However, lying during communication is an area lacking in research. This project will investigate the effects of lying on a multi-agent system in a pursuit evasion task using multi-agent reinforcement learning to learn an optimal policy, and experiment with different network configurations and techniques such as dropout and layer normalisation.

Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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