%0 Conference Paper %1 XuLiu2024 %A Xu, Linjie %A Liu, Zichuan %A Dockhorn, Alexander %A Perez-Liebana, Diego %A Wang, Jinyu %A Song, Lei %A Bian, Jiang %B Proceedings of the IEEE Conference on Games 2024 %D 2024 %K Learning Reinforcement leibnizailab myown %P 1-8 %T Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning

@inproceedings{XuLiu2024, added-at = {2024-09-09T16:00:02.000+0200}, author = {Xu, Linjie and Liu, Zichuan and Dockhorn, Alexander and Perez-Liebana, Diego and Wang, Jinyu and Song, Lei and Bian, Jiang}, biburl = {https://www.bibsonomy.org/bibtex/2f57e4edfd40f0f24704a409d44f5a161/tntl3s}, booktitle = {Proceedings of the IEEE Conference on Games 2024}, interhash = {3a0044b5eb9cb7a588b69c508684eaf0}, intrahash = {f57e4edfd40f0f24704a409d44f5a161}, keywords = {Learning Reinforcement leibnizailab myown}, pages = {1-8}, timestamp = {2024-09-09T16:00:02.000+0200}, title = {Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning}, year = 2024 }