Multi-agent Robust and Optimal Policy Learning for Data Harvesting
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914001006362624 |
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| author | Wu, Shili Zhu, Yancheng Datta, Aniruddha Andersson, Sean B. |
| author_facet | Wu, Shili Zhu, Yancheng Datta, Aniruddha Andersson, Sean B. |
| contents | We consider the problem of using multiple agents to harvest data from a collection of sensor nodes (targets) scattered across a two-dimensional environment. These targets transmit their data to the agents that move in the space above them, and our goal is for the agents to collect data from the targets as efficiently as possible while moving to their final destinations. The agents are assumed to have a continuous control action, and we leverage reinforcement learning, specifically Proximal Policy Optimization (PPO) with Lagrangian Penalty (LP), to identify highly effective solutions. Additionally, we enhance the controller's robustness by incorporating regularization at each state to smooth the learned policy. We conduct a series of simulations to demonstrate our approach and validate its performance and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-agent Robust and Optimal Policy Learning for Data Harvesting Wu, Shili Zhu, Yancheng Datta, Aniruddha Andersson, Sean B. Systems and Control We consider the problem of using multiple agents to harvest data from a collection of sensor nodes (targets) scattered across a two-dimensional environment. These targets transmit their data to the agents that move in the space above them, and our goal is for the agents to collect data from the targets as efficiently as possible while moving to their final destinations. The agents are assumed to have a continuous control action, and we leverage reinforcement learning, specifically Proximal Policy Optimization (PPO) with Lagrangian Penalty (LP), to identify highly effective solutions. Additionally, we enhance the controller's robustness by incorporating regularization at each state to smooth the learned policy. We conduct a series of simulations to demonstrate our approach and validate its performance and robustness. |
| title | Multi-agent Robust and Optimal Policy Learning for Data Harvesting |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.16490 |