Experience Replay Method with Attention for Multi-agent Reinforcement Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8864-8_59
Reference20 articles.
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2. Nguyen, T.T., Nguyen, N.D., Nahavandi, S.: Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications. IEEE Trans. Cybern. 50(9), 3826–3839 (2020)
3. Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multi-agent reinforcement learning. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(2), 156–172 (2008)
4. Luis, C.E., Schoellig, A.P.: Trajectory generation for multiagent point-to-point transitions via distributed model predictive control. IEEE Robot. Autom. Lett. 4(2), 375–382 (2019)
5. Niroui, F., Zhang, K., Kashino, Z., Nejat, G.: Deep reinforcement learning robot for search and rescue applications: exploration in unknown cluttered environments. IEEE Robot. Autom. Lett. 4(2), 610–617 (2019)
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