[37] Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. Rllib: Abstractions for distributed reinforcement learning. In International Conference on Machine Learning, pages 3053-3062, 2018.
[38] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
[39] Siyu Lin and Peter A Beling. An end-to-end optimal trade execution framework based on proximal policy optimization. In International Joint Conference on Artificial Intelligence, pages 4548-4554, 2020.
[40] Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, and Christina Dan Wang. Finrl: Deep reinforcement learning framework to automate trading in quantitative finance. In ACM International Conference on AI in Finance, pages 1-9, 2021.
[41] Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, and Jian Guo. FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning. In Advances in Neural Information Processing Systems (Datasets and Benchmarks Track), 2022.
[42] Yang Liu, Qi Liu, Hongke Zhao, Zhen Pan, and Chuanren Liu. Adaptive quantitative trading: An imitative deep reinforcement learning approach. In AAAI Conference on Artificial Intelligence, pages 2128-2135, 2020.
[43] Malik Magdon-Ismail and Amir F Atiya. Maximum drawdown. Risk Magazine, 17(10):99-102, 2004.
[44] Velayoudoum Marimoutou, Bechir Raggad, and Abdelwahed Trabelsi. Extreme value theory and value at risk: application to oil market. Energy Economics, 31(4):519-530, 2009.
[45] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
[46] Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning, pages 1928-1937, 2016.
[47] Ralph Neuneier. Optimal asset allocation using adaptive dynamic programming. In Advances in Neural Information Processing Systems, pages 952-958, 1996.
[48] Yuriy Nevmyvaka, Yi Feng, and Michael Kearns. Reinforcement learning for optimized trade execution. In International Conference on Machine Learning (ICML), pages 673-680, 2006.
[49] Marco Pagano. Financial markets and growth: an overview. European Economic Review, 37 (2-3):613-622, 1993.
[50] Feiyang Pan, Tongzhe Zhang, Ling Luo, Jia He, and Shuoling Liu. Learn continuously, act discretely: Hybrid action-space reinforcement learning for optimal execution. In International Joint Conference on Artificial Intelligence, pages 3912–3918, 2022.
[51] Robert Pardo. The evaluation and optimization of trading strategies, volume 314. John Wiley & Sons, 2011.
[52] Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, and Bo An. Earn-hft: Efficient hierarchical reinforcement learning for high frequency trading. arXiv preprint arXiv:2309.12891, 2023.
[53] Rong-Jun Qin, Xingyuan Zhang, Songyi Gao, Xiong-Hui Chen, Zewen Li, Weinan Zhang, and Yang Yu. Neorl: A near real-world benchmark for offline reinforcement learning. Advances in Neural Information Processing Systems, 35:24753-24765, 2022.