Lying in pursuit evasion task with multi-agent reinforcement learning
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...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157325 |
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| 總結: | 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.
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