Multi-agent reinforcement learning for complex sequential decision-making
Many tasks involve multiple agents and require sequential decision-making policies to achieve common goals, such as football games, real-time strategy games, and traffic light control in the road network. To obtain the policies of all agents, these problems can be modeled as multi-agent systems and...
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| 格式: | Thesis-Doctor of Philosophy |
| 語言: | English |
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Nanyang Technological University
2024
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| 在線閱讀: | https://hdl.handle.net/10356/173035 |
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| 機構: | Nanyang Technological University |
| 語言: | English |