[26] Danilo Jimenez Rezende and Shakir Mohamed. Variational inference with normalizing flows. In Francis R. Bach and David M. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, volume 37 of JMLR Workshop and Conference Proceedings, pages 1530-1538. JMLR.org, 2015. URL http://proceedings.mlr.press/v37/rezende15.html.
[27] Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, and Milind Tambe. Solving online threat screening games using constrained action space reinforcement learning. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, February 7-12, 2020, pages 2226-2235. AAAI Press, 2020. URL https://aaai.org/ojs/index.php/AAAI/article/view/5599.
[28] Satinder P Singh, Tommi Jaakkola, and Michael I Jordan. Learning without state-estimation in partially observable markovian decision processes. In Machine Learning Proceedings 1994, pages 284–292. Elsevier, 1994.
[29] Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. URL https://openreview.net/forum?id=PxTIG12RRHS.
[30] Yunhao Tang and Shipra Agrawal. Discretizing continuous action space for on-policy optimization. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 5981-5988. AAAI Press, 2020. URL https://aaai.org/ojs/index.php/AAAI/article/view/6059.
[31] Justin K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S. Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, Niall L. Williams, Yashas Lokesh, and Praveen Ravi. Pettingzoo: Gym for multi-agent reinforcement learning. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 15032-15043, 2021. URL https://proceedings.neurips.cc/paper/2021/bitstream/7ed2d3454c5eea71148b11d0c25104ff-Abstract.html.
[32] Ivan B Vermeulen, Sander M Bohte, Sylvia G Elkuizen, Han Lameris, Piet JM Bakker, and Han La Poutré. Adaptive resource allocation for efficient patient scheduling. Artificial intelligence in medicine, 46(1):67-80, 2009.
[33] Patrick Nadeem Ward, Ariella Smofsky, and Avishek Joey Bose. Improving exploration in soft-actor-critic with normalizing flows policies. ArXiv preprint, abs/1906.02771, 2019. URL https://arxiv.org/abs/1906.02771.
[34] Mason Wright, Yongzhao Wang, and Michael P Wellman. Iterated deep reinforcement learning in games: History-aware training for improved stability. In EC, pages 617-636, 2019.
[35] Haifeng Xu. The mysteries of security games: Equilibrium computation becomes combinatorial algorithm design. In Proceedings of the 2016 ACM Conference on Economics and Computation, pages 497-514, 2016.
[36] Yiming Zhang, Quan Ho Vuong, Kenny Song, Xiao-Yue Gong, and Keith W Ross. Efficient entropy for policy gradient with multidimensional action space. ArXiv preprint, abs/1806.00589, 2018. URL https://arxiv.org/abs/1806.00589.