Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F1840

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 661–666 (2024)

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Abstract. In multi-agent reinforcement learning scenarios, independent learning, where agents learn independently based on their observations, is often preferred for its scalability and simplicity compared to centralized training. However, it faces significant challenges due to the non-stationary nature of the environment from each agent's perspective.

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