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- Publisher Website: 10.1109/ICCW.2019.8756984
- Scopus: eid_2-s2.0-85070328210
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Conference Paper: The application of multi-agent reinforcement learning in UAV networks
Title | The application of multi-agent reinforcement learning in UAV networks |
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Authors | |
Issue Date | 2019 |
Citation | 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings, 2019, article no. 8756984 How to Cite? |
Abstract | This article investigates autonomous resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. To model the uncertainty of environments, we formulate the long-term resource allocation problem as a stochastic game, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Furthermore, we propose a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. |
Persistent Identifier | http://hdl.handle.net/10722/349343 |
DC Field | Value | Language |
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dc.contributor.author | Cui, Jingjing | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Nallanathan, Arumugam | - |
dc.date.accessioned | 2024-10-17T06:57:54Z | - |
dc.date.available | 2024-10-17T06:57:54Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings, 2019, article no. 8756984 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349343 | - |
dc.description.abstract | This article investigates autonomous resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. To model the uncertainty of environments, we formulate the long-term resource allocation problem as a stochastic game, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Furthermore, we propose a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. | - |
dc.language | eng | - |
dc.relation.ispartof | 2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings | - |
dc.title | The application of multi-agent reinforcement learning in UAV networks | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICCW.2019.8756984 | - |
dc.identifier.scopus | eid_2-s2.0-85070328210 | - |
dc.identifier.spage | article no. 8756984 | - |
dc.identifier.epage | article no. 8756984 | - |