Target localization using multi-agent deep reinforcement learning with proximal policy optimization
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- Authors
- Alagha, A., Singh, S., Mizouni, R., Bentahar, J., & Otrok, H.
- Affiliations
- Concordia University (CA)
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Alagha, Ahmed
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 136
- Pages
- 342-357
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W4283768545
- Cited by
- 72
- References
- 59
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 19 · 2023: 16 · 2022: 2
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Future Generation Computer Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- target localization, multi-agent deep reinforcement learning, proximal policy optimization, actor-critic, convolutional neural networks, centralized training decentralized execution, team-based rewards, agent cooperation, sensor fusion, unmanned aerial vehicles, image downscaling, Gaussian filtering
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