@article{DQN,
  added-at = {2022-01-12T14:28:12.000+0100},
  author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and van den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
  biburl = {https://www.bibsonomy.org/bibtex/2e3bd772f62209ea8283e242f993d3edf/stumme},
  doi = {10.1038/nature16961},
  interhash = {48430c7891aaf9fe2582faa8f5d076c1},
  intrahash = {e3bd772f62209ea8283e242f993d3edf},
  journal = {Nature},
  keywords = {alphaGo deep learning},
  month = jan,
  number = 7587,
  pages = {484--489},
  publisher = {Springer Science and Business Media {LLC}},
  timestamp = {2022-01-12T14:28:12.000+0100},
  title = {Mastering the game of Go with deep neural networks and tree search},
  url = {https://doi.org/10.1038%2Fnature16961},
  volume = 529,
  year = 2016
}
@inproceedings{TRPO,
  abstract = {We describe an iterative procedure for optimizing policies, with guaranteed monotonic improvement. By making several approximations to the theoretically-justified procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effective for optimizing large nonlinear policies such as neural networks. Our experiments demonstrate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. Despite its approximations that deviate from the theory, TRPO tends to give monotonic improvement, with little tuning of hyperparameters. },
  added-at = {2019-12-16T18:19:24.000+0100},
  author = {Schulman, John and Levine, Sergey and Abbeel, Pieter and Jordan, Michael I. and Moritz, Philipp},
  biburl = {https://www.bibsonomy.org/bibtex/27462ef83a282ebeba5963e16176f9e29/lanteunis},
  booktitle = {ICML},
  crossref = {conf/icml/2015},
  editor = {Bach, Francis R. and Blei, David M.},
  ee = {http://proceedings.mlr.press/v37/schulman15.html},
  interhash = {a8e265b2ea2ac6ead6b6e17a5a4bdc09},
  intrahash = {7462ef83a282ebeba5963e16176f9e29},
  keywords = {DRLAlgoComparison reinforcement_learning},
  pages = {1889-1897},
  publisher = {JMLR.org},
  series = {JMLR Workshop and Conference Proceedings},
  timestamp = {2019-12-16T21:10:27.000+0100},
  title = {Trust Region Policy Optimization.},
  url = {http://dblp.uni-trier.de/db/conf/icml/icml2015.html#SchulmanLAJM15},
  volume = 37,
  year = 2015
}
@inproceedings{DDPG,
  added-at = {2019-07-11T18:31:51.000+0200},
  author = {Lillicrap, Timothy P. and Hunt, Jonathan J. and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
  biburl = {https://www.bibsonomy.org/bibtex/22708c349821330660afb992aec2be5d1/lanteunis},
  booktitle = {ICLR},
  crossref = {conf/iclr/2016},
  editor = {Bengio, Yoshua and LeCun, Yann},
  ee = {http://arxiv.org/abs/1509.02971},
  interhash = {b791167abe535c8525f6a9bf62fcc1ab},
  intrahash = {2708c349821330660afb992aec2be5d1},
  keywords = {final reinforcement thema:reinforcement_learning_recommender},
  timestamp = {2019-07-11T18:31:51.000+0200},
  title = {Continuous control with deep reinforcement learning.},
  url = {http://dblp.uni-trier.de/db/conf/iclr/iclr2016.html#LillicrapHPHETS15},
  year = 2016
}
@inproceedings{Platform,
  added-at = {2019-11-19T00:00:00.000+0100},
  author = {Masson, Warwick and Ranchod, Pravesh and Konidaris, George Dimitri},
  biburl = {https://www.bibsonomy.org/bibtex/2c81ae448fa2e8a18cdd313fe3fa20673/dblp},
  booktitle = {AAAI},
  crossref = {conf/aaai/2016},
  editor = {Schuurmans, Dale and Wellman, Michael P.},
  ee = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11981},
  interhash = {e94466873642a80f0306564d64c62502},
  intrahash = {c81ae448fa2e8a18cdd313fe3fa20673},
  isbn = {978-1-57735-760-5},
  keywords = {dblp},
  pages = {1934-1940},
  publisher = {AAAI Press},
  timestamp = {2019-11-20T11:45:04.000+0100},
  title = {Reinforcement Learning with Parameterized Actions.},
  url = {http://dblp.uni-trier.de/db/conf/aaai/aaai2016.html#MassonRK16},
  year = 2016
}
@inproceedings{PADDPG,
  added-at = {2019-07-25T00:00:00.000+0200},
  author = {Hausknecht, Matthew J. and Stone, Peter},
  biburl = {https://www.bibsonomy.org/bibtex/2b7c258ff9ceac49b142315f920f56fbc/dblp},
  booktitle = {ICLR (Poster)},
  crossref = {conf/iclr/2016},
  editor = {Bengio, Yoshua and LeCun, Yann},
  ee = {http://arxiv.org/abs/1511.04143},
  interhash = {bb90f08c36bda09ae7cc4e434bd57154},
  intrahash = {b7c258ff9ceac49b142315f920f56fbc},
  keywords = {dblp},
  timestamp = {2019-07-26T11:43:33.000+0200},
  title = {Deep Reinforcement Learning in Parameterized Action Space.},
  url = {http://dblp.uni-trier.de/db/conf/iclr/iclr2016.html#HausknechtS15a},
  year = 2016
}
@inproceedings{PAMDP,
  title={Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces},
  author={Massaroli, Stefano and Poli, Michael and Bakhtiyarov, Sanzhar and Yamashita, Atsushi and Asama, Hajime and Park, Jinkyoo},
  booktitle={ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations},
  year={2020}
}
@article{PDQN,
  title={Parametrized deep q-networks learning: Reinforcement learning with discrete-continuous hybrid action space},
  author={Xiong, Jiechao and Wang, Qing and Yang, Zhuoran and Sun, Peng and Han, Lei and Zheng, Yang and Fu, Haobo and Zhang, Tong and Liu, Ji and Liu, Han},
  journal={arXiv preprint arXiv:1810.06394},
  year={2018}
}

@article{Hyar,
  title={Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation},
  author={Li, Boyan and Tang, Hongyao and Zheng, Yan and Hao, Jianye and Li, Pengyi and Wang, Zhen and Meng, Zhaopeng and Wang, Li},
  journal={arXiv preprint arXiv:2109.05490},
  year={2021}
}
@inproceedings{HPPO,
  added-at = {2019-08-20T00:00:00.000+0200},
  author = {Fan, Zhou and Su, Rui and Zhang, Weinan and Yu, Yong},
  biburl = {https://www.bibsonomy.org/bibtex/2fb53732e650d85aa81b8aac3f6805589/dblp},
  booktitle = {IJCAI},
  crossref = {conf/ijcai/2019},
  editor = {Kraus, Sarit},
  ee = {https://doi.org/10.24963/ijcai.2019/316},
  interhash = {76d6bfec302f788deb1c33fc3263d0c0},
  intrahash = {fb53732e650d85aa81b8aac3f6805589},
  keywords = {dblp},
  pages = {2279-2285},
  publisher = {ijcai.org},
  timestamp = {2019-08-21T11:47:01.000+0200},
  title = {Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space.},
  url = {http://dblp.uni-trier.de/db/conf/ijcai/ijcai2019.html#FanS0019},
  year = 2019
}
@article{peng,
  title={Improving Action Branching for Deep Reinforcement Learning with A Multi-dimensional Hybrid Action Space},
  author={Peng, Laige and Tsuruoka, Yoshimasa},
  year={2019}
}
@article{Delalleau,
  added-at = {2020-01-03T00:00:00.000+0100},
  author = {Delalleau, Olivier and Peter, Maxim and Alonso, Eloi and Logut, Adrien},
  biburl = {https://www.bibsonomy.org/bibtex/20771114335bc4b08d5c41fe316be34e9/dblp},
  ee = {http://arxiv.org/abs/1912.11077},
  interhash = {5382de08033570456a054a5708dbcc81},
  intrahash = {0771114335bc4b08d5c41fe316be34e9},
  journal = {CoRR},
  keywords = {dblp},
  timestamp = {2020-01-04T11:38:53.000+0100},
  title = {Discrete and Continuous Action Representation for Practical RL in Video Games.},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1912.html#abs-1912-11077},
  volume = {abs/1912.11077},
  year = 2019
}
@inproceedings{Ma,
  title={Distributed Reinforcement Learning with Self-Play in Parameterized Action Space},
  author={Ma, Jun and Yao, Shunyi and Chen, Guangda and Song, Jiakai and Ji, Jianmin},
  booktitle={2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  pages={1178--1185},
  year={2021},
  organization={IEEE}
}
@article{MAPQN,
  added-at = {2019-11-19T00:00:00.000+0100},
  author = {Bester, Craig J. and James, Steven D. and Konidaris, George Dimitri},
  biburl = {https://www.bibsonomy.org/bibtex/229eb9b4a49b6e10343a7e33a23689383/dblp},
  ee = {http://arxiv.org/abs/1905.04388},
  interhash = {ad68772b31f389519e943869b3020b59},
  intrahash = {29eb9b4a49b6e10343a7e33a23689383},
  journal = {CoRR},
  keywords = {dblp},
  timestamp = {2019-11-20T11:39:13.000+0100},
  title = {Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces.},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1905.html#abs-1905-04388},
  volume = {abs/1905.04388},
  year = 2019
}
@article{Chandak,
  added-at = {2019-06-13T00:00:00.000+0200},
  author = {Chandak, Yash and Theocharous, Georgios and Kostas, James and Jordan, Scott M. and Thomas, Philip S.},
  biburl = {https://www.bibsonomy.org/bibtex/2a93d99ac2616a04d502b96ce02e9447e/dblp},
  ee = {http://arxiv.org/abs/1902.00183},
  interhash = {f7a4ac39302b1a725ebf68c473870177},
  intrahash = {a93d99ac2616a04d502b96ce02e9447e},
  journal = {CoRR},
  keywords = {dblp},
  timestamp = {2019-06-14T11:39:49.000+0200},
  title = {Learning Action Representations for Reinforcement Learning.},
  url = {http://dblp.uni-trier.de/db/journals/corr/corr1902.html#abs-1902-00183},
  volume = {abs/1902.00183},
  year = 2019
}
@inproceedings{HHQN,
  added-at = {2019-08-20T00:00:00.000+0200},
  author = {Fu, Haotian and Tang, Hongyao and Hao, Jianye and Lei, Zihan and Chen, Yingfeng and Fan, Changjie},
  biburl = {https://www.bibsonomy.org/bibtex/2065cf95ceb97be7aa77d92066369fa0f/dblp},
  booktitle = {IJCAI},
  crossref = {conf/ijcai/2019},
  editor = {Kraus, Sarit},
  ee = {https://doi.org/10.24963/ijcai.2019/323},
  interhash = {2e4a67267c4e77f61cd219d8ea6f38c2},
  intrahash = {065cf95ceb97be7aa77d92066369fa0f},
  keywords = {dblp},
  pages = {2329-2335},
  publisher = {ijcai.org},
  timestamp = {2019-08-21T11:47:01.000+0200},
  title = {Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces.},
  url = {http://dblp.uni-trier.de/db/conf/ijcai/ijcai2019.html#FuTHLCF19},
  year = 2019
}
@inproceedings{Lee,
  title={Set transformer: A framework for attention-based permutation-invariant neural networks},
  author={Lee, Juho and Lee, Yoonho and Kim, Jungtaek and Kosiorek, Adam and Choi, Seungjin and Teh, Yee Whye},
  booktitle={International conference on machine learning},
  pages={3744--3753},
  year={2019},
  organization={PMLR}
}
@incollection{vaswani,
  added-at = {2020-12-14T00:29:38.000+0100},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Łukasz and Polosukhin, Illia},
  biburl = {https://www.bibsonomy.org/bibtex/2eebc38736c3f890f1f7dcdd94a791f3b/mcreinhardt},
  booktitle = {Advances in Neural Information Processing Systems 30},
  editor = {Guyon, I. and Luxburg, U. V. and Bengio, S. and Wallach, H. and Fergus, R. and Vishwanathan, S. and Garnett, R.},
  interhash = {b23d83da70543e00f9240cc009f1fcfa},
  intrahash = {eebc38736c3f890f1f7dcdd94a791f3b},
  keywords = {final thema:contextlengthextension},
  pages = {5998–6008},
  publisher = {Curran Associates, Inc.},
  timestamp = {2020-12-14T00:57:33.000+0100},
  title = {Attention is All you Need},
  url = {https://papers.nips.cc/paper/7181-attention-is-all-you-need},
  year = 2017
}

@article{Hu,
  title={Updet: Universal multi-agent reinforcement learning via policy decoupling with transformers},
  author={Hu, Siyi and Zhu, Fengda and Chang, Xiaojun and Liang, Xiaodan},
  journal={arXiv preprint arXiv:2101.08001},
  year={2021}
}
@book{Oliehoek,
  added-at = {2020-05-24T20:36:48.000+0200},
  author = {Oliehoek, Frans A. and Amato, Christopher},
  biburl = {https://www.bibsonomy.org/bibtex/2ce3c73068d5c4b24cf76bc4a88dbbe63/marcondg},
  interhash = {51f754a79bc241e4b0b239ef3bdbb27a},
  intrahash = {ce3c73068d5c4b24cf76bc4a88dbbe63},
  isbn = {3319289276},
  keywords = {dec-pomdp, survey},
  location = {Berlin, Germany},
  publisher = {Springer},
  timestamp = {2020-05-24T20:36:48.000+0200},
  title = {A concise introduction to decentralized {POMDP}s.},
  url = {http://www.worldcat.org/isbn/3319289276},
  year = 2016
}
@inproceedings{MADDPG,
  added-at = {2020-03-06T00:00:00.000+0100},
  author = {Lowe, Ryan and Wu, Yi and Tamar, Aviv and Harb, Jean and Abbeel, Pieter and Mordatch, Igor},
  biburl = {https://www.bibsonomy.org/bibtex/22acadcf22d42b3763e27146e4b07b590/dblp},
  booktitle = {NIPS},
  crossref = {conf/nips/2017},
  editor = {Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy and Wallach, Hanna M. and Fergus, Rob and Vishwanathan, S. V. N. and Garnett, Roman},
  ee = {http://papers.nips.cc/paper/7217-multi-agent-actor-critic-for-mixed-cooperative-competitive-environments},
  interhash = {ad75ef0ceb4ebd70717239253e68572e},
  intrahash = {2acadcf22d42b3763e27146e4b07b590},
  keywords = {dblp},
  pages = {6379-6390},
  timestamp = {2020-03-07T11:55:59.000+0100},
  title = {Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.},
  url = {http://dblp.uni-trier.de/db/conf/nips/nips2017.html#LoweWTHAM17},
  year = 2017
}
@inproceedings{Qmix,
  title={Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning},
  author={Rashid, Tabish and Samvelyan, Mikayel and Schroeder, Christian and Farquhar, Gregory and Foerster, Jakob and Whiteson, Shimon},
  booktitle={International conference on machine learning},
  pages={4295--4304},
  year={2018},
  organization={PMLR}
}
@article{Facmac,
  title={Facmac: Factored multi-agent centralised policy gradients},
  author={Peng, Bei and Rashid, Tabish and Schroeder de Witt, Christian and Kamienny, Pierre-Alexandre and Torr, Philip and B{\"o}hmer, Wendelin and Whiteson, Shimon},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12208--12221},
  year={2021}
}
@inproceedings{Discrete,
  added-at = {2020-06-04T00:00:00.000+0200},
  author = {Tang, Yunhao and Agrawal, Shipra},
  biburl = {https://www.bibsonomy.org/bibtex/2a4585536f8b21a655d25ee88fa6d63bb/dblp},
  booktitle = {AAAI},
  crossref = {conf/aaai/2020},
  ee = {https://aaai.org/ojs/index.php/AAAI/article/view/6059},
  interhash = {0b7675b0ac4e99087240cbfa279159cb},
  intrahash = {a4585536f8b21a655d25ee88fa6d63bb},
  isbn = {978-1-57735-823-7},
  keywords = {dblp},
  pages = {5981-5988},
  publisher = {AAAI Press},
  timestamp = {2020-06-05T12:32:53.000+0200},
  title = {Discretizing Continuous Action Space for On-Policy Optimization.},
  url = {http://dblp.uni-trier.de/db/conf/aaai/aaai2020.html#Tang020},
  year = 2020
}
@inproceedings{Cheng,
  title={On the connection between local attention and dynamic depth-wise convolution},
  author={Han, Qi and Fan, Zejia and Dai, Qi and Sun, Lei and Cheng, Ming-Ming and Liu, Jiaying and Wang, Jingdong},
  booktitle={International Conference on Learning Representations},
  year={2021}
}
@inproceedings{Swin,
  title={Swin transformer: Hierarchical vision transformer using shifted windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10012--10022},
  year={2021}
}
@inproceedings{IQL,
  title={Multi-agent reinforcement learning: Independent vs. cooperative agents},
  author={Tan, Ming},
  booktitle={Proceedings of the tenth international conference on machine learning},
  pages={330--337},
  year={1993}
}
@article{Qatten,
  title={Qatten: A general framework for cooperative multiagent reinforcement learning},
  author={Yang, Yaodong and Hao, Jianye and Liao, Ben and Shao, Kun and Chen, Guangyong and Liu, Wulong and Tang, Hongyao},
  journal={arXiv preprint arXiv:2002.03939},
  year={2020}
}
@article{QPLEX,
  title={Qplex: Duplex dueling multi-agent q-learning},
  author={Wang, Jianhao and Ren, Zhizhou and Liu, Terry and Yu, Yang and Zhang, Chongjie},
  journal={arXiv preprint arXiv:2008.01062},
  year={2020}
}

@inproceedings{haarnoja2018soft,
  title={Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  booktitle={International conference on machine learning},
  pages={1861--1870},
  year={2018},
  organization={PMLR}
}

@inproceedings{fujimoto2018addressing,
  title={Addressing function approximation error in actor-critic methods},
  author={Fujimoto, Scott and Hoof, Herke and Meger, David},
  booktitle={International conference on machine learning},
  pages={1587--1596},
  year={2018},
  organization={PMLR}
}

@book{oliehoek2016concise,
  title={A concise introduction to decentralized POMDPs},
  author={Oliehoek, Frans A and Amato, Christopher},
  year={2016},
  publisher={Springer}
}

@article{jang2016categorical,
  title={Categorical reparameterization with gumbel-softmax},
  author={Jang, Eric and Gu, Shixiang and Poole, Ben},
  journal={arXiv preprint arXiv:1611.01144},
  year={2016}
}

@article{VDN,
  title={Value-decomposition networks for cooperative multi-agent learning},
  author={Sunehag, Peter and Lever, Guy and Gruslys, Audrunas and Czarnecki, Wojciech Marian and Zambaldi, Vinicius and Jaderberg, Max and Lanctot, Marc and Sonnerat, Nicolas and Leibo, Joel Z and Tuyls, Karl and others},
  journal={arXiv preprint arXiv:1706.05296},
  year={2017}
}

@ARTICLE{MCTS,
  author={Browne, Cameron B. and Powley, Edward and Whitehouse, Daniel and Lucas, Simon M. and Cowling, Peter I. and Rohlfshagen, Philipp and Tavener, Stephen and Perez, Diego and Samothrakis, Spyridon and Colton, Simon},
  journal={IEEE Transactions on Computational Intelligence and AI in Games}, 
  title={A Survey of Monte Carlo Tree Search Methods}, 
  year={2012},
  volume={4},
  number={1},
  pages={1-43},
  doi={10.1109/TCIAIG.2012.2186810}}
@article{UCT,
  title={Multi-armed bandits with episode context},
  author={Rosin, Christopher D},
  journal={Annals of Mathematics and Artificial Intelligence},
  volume={61},
  number={3},
  pages={203--230},
  year={2011},
  publisher={Springer}
}

@article{auer2002finite,
  title={Finite-time analysis of the multiarmed bandit problem},
  author={Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
  journal={Machine learning},
  volume={47},
  number={2},
  pages={235--256},
  year={2002},
  publisher={Springer}
}

@inproceedings{gpt,
  title={Improving Language Understanding by Generative Pre-Training},
  author={Alec Radford and Karthik Narasimhan},
  year={2018},
  url={https://api.semanticscholar.org/CorpusID:49313245}
}