Team-wise effective communication in multi-agent reinforcement learning

Author:

Yang Ming,Zhao Kaiyan,Wang Yiming,Dong Renzhi,Du Yali,Liu Furui,Zhou Mingliang,U Leong Hou

Publisher

Springer Science and Business Media LLC

Reference56 articles.

1. Zivan, R., Rachmut, B., Perry, O., et al. (2023). Effect of asynchronous execution and imperfect communication on max-sum belief propagation. Autonomous Agents and Multi-Agent Systems, 37, 40. https://doi.org/10.1007/s10458-023-09621-w

2. Yang, M., Wang, Y., Yu, Y., Zhou, M., & U, L. H. (2023). MixLight: Mixed-agent cooperative reinforcement learning for traffic light control. IEEE Transactions on Industrial Informatics, 20(2), 2653–2661. https://doi.org/10.1109/TII.2023.3296910

3. Foerster, J. N., Assael, Y. M., Freitas, N., & Whiteson, S. (2016). Learning to communicate with deep multi-agent reinforcement learning. In Proceedings of the 30th international conference on neural information processing systems (pp. 2145–2153).

4. Sukhbaatar, S., Fergus, R., et al. (2016). Learning multiagent communication with backpropagation. Advances in Neural Information Processing Systems, 29, 2244–2252.

5. Jiang, J., & Lu, Z. (2018). Learning attentional communication for multi-agent cooperation. Advances in Neural Information Processing Systems, 31, 7254–7264.

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