An Efficient Multi-agent Deep Deterministic Policy Gradient-Based 3D Dynamic Coverage Algorithm
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2757-5_9
Reference9 articles.
1. Liu, C.H., Chen, Z., Tang, J., Xu, J., Piao, C.: Energy-efficient UAV control for effective and fair communication coverage: a deep reinforcement learning approach. IEEE J. Sel. Areas Commun. 36(9), 2059–2070 (2018)
2. Hanh, N.T., Binh, H.T.T., Hoai, N.X., Palaniswami, M.S.: An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Inf. Sci. 488, 58–75 (2019)
3. Din, A., Ismail, M.Y., Shah, B., Babar, M., Ali, F., Baig, S.U.: A deep reinforcement learning-based multi-agent area coverage control for smart agriculture. Comput. Electr. Eng. 101, 108089 (2022)
4. Shakhatreh, H., Khreishah, A., Khalil, I.: Indoor mobile coverage problem using UAVs. IEEE Syst. J. 12(4), 3837–3848 (2018)
5. Bialas, J., Doller, M.:Coverage path planning for unmanned aerial vehicles in complex 3D environments with deep reinforcement learning. In: Proceedings 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO), Jinghong, China, pp. 1080-1085 (2022)
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