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Reinforcement Learning

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* [https://dblp.org/pid/233/8144.html Indu John], [https://scholar.google.co.in/citations?user=1QlrvHkAAAAJ&hl=en Chandramouli Kamanchi], [[Shalabh Bhatnagar]] ('''2020'''). ''Generalized Speedy Q-Learning''. [[IEEE#CSL|IEEE Control Systems Letters]], Vol. 4, No. 3, [https://arxiv.org/abs/1911.00397 arXiv:1911.00397]
* [[Takuya Hiraoka]], [https://dblp.org/pers/hd/i/Imagawa:Takahisa Takahisa Imagawa], [https://dblp.org/pers/hd/t/Tangkaratt:Voot Voot Tangkaratt], [https://dblp.org/pers/hd/o/Osa:Takayuki Takayuki Osa], [https://dblp.org/pers/hd/o/Onishi:Takashi Takashi Onishi], [https://dblp.org/pers/hd/t/Tsuruoka:Yoshimasa Yoshimasa Tsuruoka] ('''2020'''). ''Meta-Model-Based Meta-Policy Optimization''. [https://arxiv.org/abs/2006.02608 arXiv:2006.02608]
* [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588 <ref>[https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?fbclid=IwAR3mSwrn1YXDKr9uuGm2GlFKh76wBilex7f8QvBiQecwiVmAvD6Bkyjx-rE MuZero: Mastering Go, chess, shogi and Atari without rules]</ref> <ref>[https://github.com/koulanurag/muzero-pytorch GitHub - koulanurag/muzero-pytorch: Pytorch Implementation of MuZero]</ref>
* [[Tristan Cazenave]], [[Yen-Chi Chen]], [[Guan-Wei Chen]], [[Shi-Yu Chen]], [[Xian-Dong Chiu]], [[Julien Dehos]], [[Maria Elsa]], [[Qucheng Gong]], [[Hengyuan Hu]], [[Vasil Khalidov]], [[Cheng-Ling Li]], [[Hsin-I Lin]], [[Yu-Jin Lin]], [[Xavier Martinet]], [[Vegard Mella]], [[Jeremy Rapin]], [[Baptiste Roziere]], [[Gabriel Synnaeve]], [[Fabien Teytaud]], [[Olivier Teytaud]], [[Shi-Cheng Ye]], [[Yi-Jun Ye]], [[Shi-Jim Yen]], [[Sergey Zagoruyko]] ('''2020'''). ''Polygames: Improved zero learning''. [[ICGA Journal#42_4|ICGA Journal, Vol. 42, No. 4]], [https://arxiv.org/abs/2001.09832 arXiv:2001.09832], [https://arxiv.org/abs/2001.09832 arXiv:2001.09832]
* [[Matthia Sabatelli]], [https://github.com/glouppe Gilles Louppe], [https://scholar.google.com/citations?user=tyFTsmIAAAAJ&hl=en Pierre Geurts], [[Marco Wiering]] ('''2020'''). ''The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms''. [https://dblp.org/db/conf/ijcnn/ijcnn2020.html#SabatelliLGW20 IJCNN 2020] <ref>[https://github.com/paintception/Deep-Quality-Value-DQV-Learning- GitHub - paintception/Deep-Quality-Value-DQV-Learning-: DQV-Learning: a novel faster synchronous Deep Reinforcement Learning algorithm]</ref>
** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/games open_spiel/open_spiel/games at master · deepmind/open_spiel · GitHub]
*** [https://github.com/deepmind/open_spiel/tree/master/open_spiel/games/chess open_spiel/open_spiel/games/chess at master · deepmind/open_spiel · GitHub]
* [https://github.com/koulanurag/muzero-pytorch GitHub - koulanurag/muzero-pytorch: Pytorch Implementation of MuZero] <ref>[[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Thomas Hubert]], [[Karen Simonyan]], [[Laurent Sifre]], [[Simon Schmitt]], [[Arthur Guez]], [[Edward Lockhart]], [[Demis Hassabis]], [[Thore Graepel]], [[Timothy Lillicrap]], [[David Silver]] ('''2020'''). ''[https://www.nature.com/articles/s41586-020-03051-4 Mastering Atari, Go, chess and shogi by planning with a learned model]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 588</ref>
* [https://github.com/YeWR/EfficientZero GitHub - YeWR/EfficientZero: Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021] <ref>[[Weirui Ye]], [[Shaohuai Liu]], [[Thanard Kurutach]], [[Pieter Abbeel]], [[Yang Gao]] ('''2021'''). ''Mastering Atari Games with Limited Data''. [https://arxiv.org/abs/2111.00210 arXiv:2111.00210]</ref>

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