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Reference42 articles.
1. Ali MH, Öztük S (2023) Efficient congestion control in communications using novel weighted ensemble deep reinforcement learning. Comput Electr Eng 110(108):811. https://doi.org/10.1016/j.compeleceng.2023.108811. www.sciencedirect.com/science/article/pii/S0045790623002355
2. Anschel O, Baram N, Shimkin N (2017) Averaged-dqn: variance reduction and stabilization for deep reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. JMLR.org, Sydney, NSW, Australia, ICML’17, pp 176–185
3. Arulkumaran K, Deisenroth MP, Brundage M et al (2017) Deep reinforcement learning: a brief survey. IEEE Signal Process Magn 34(6):26–38. https://doi.org/10.1109/MSP.2017.2743240
4. Brockman G, Cheung V, Pettersson L et al (2016) Openai gym. arXiv:1606.01540
5. Cardeñoso Fernandez F, Caarls W (2018) Parameters tuning and optimization for reinforcement learning algorithms using evolutionary computing. In: 2018 International Conference on Information Systems and Computer Science (INCISCOS). IEEE, Quito, Equador, pp 301–305, https://doi.org/10.1109/INCISCOS.2018.00050