[34] Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al. A generalist agent. arXiv preprint arXiv:2205.06175, 2022.
[35] Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob Foerster, Julian Togelius, Kyunghyun Cho, and Joan Bruna. Pommerman: A multi-agent playground. arXiv preprint arXiv:1809.07124, 2018.
[36] Sebastian Risi and Julian Togelius. Increasing generality in machine learning through procedural content generation. Nature Machine Intelligence, 2, 08 2020. doi: 10.1038/s42256-020-0208-z.
[37] Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon White-son. The starcraft multi-agent challenge. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pages 2186–2188, 2019.
[38] Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Kuttler, Edward Grefenstette, and Tim Roktaschel. Minihack the planet: A sandbox for open-ended reinforcement learning research. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021.
[39] Juergen Schmidhuber. Reinforcement learning upside down: Don't predict rewards—just map them to actions. arXiv preprint arXiv:1912.02875, 2019.
[40] Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International Conference on Machine Learning, pages 5887-5896. PMLR, 2019.
[41] Joseph Suarez, Yilun Du, Phillip Isola, and Igor Mordatch. Neural mmo: A massively multiagent game environment for training and evaluating intelligent agents. arXiv preprint arXiv:1903.00784, 2019.
[42] Mingfei Sun, Vitaly Kurin, Guoqing Liu, Sam Devlin, Tao Qin, Katja Hofmann, and Shimon Whiteson. You may not need ratio clipping in ppo. arXiv preprint arXiv:2202.00079, 2022.
[43] Mingfei Sun, Sam Devlin, Jacob Beck, Katja Hofmann, and Shimon Whiteson. Trust region bounds for decentralized ppo under non-stationarity. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, AAMAS '23, page 5-13, Richland, SC, 2023. International Foundation for Autonomous Agents and Multiagent Systems. ISBN 9781450394321.
[44] Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z Leibo, Karl Tuyls, et al. Value-decomposition networks for cooperative multi-agent learning based on team reward. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pages 2085–2087, 2018.
[45] Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, and Nicolas Usunier. Torchcraft: a library for machine learning research on real-time strategy games, 2016. URL https://arxiv.org/abs/1611.00625.
[46] Justin K Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, et al. Pettingzoo: Gym for multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 2021.
[47] Ashish Vavwani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[48] Eugene Vinitsky, Natasha Jaques, Joel Leibo, Antonio Castenada, and Edward Hughes. An open source implementation of sequential social dilemma games. https://github.com/eugenevinitsky/sequential_social_dilemma Games/issues/182, 2019. GitHub repository.