Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty
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- Olivier Guéant & Iuliia Manziuk, 2019. "Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality," Post-Print hal-03252505, HAL.
- V. S. Borkar, 2002. "Q-Learning for Risk-Sensitive Control," Mathematics of Operations Research, INFORMS, vol. 27(2), pages 294-311, May.
- Olivier Gu'eant & Iuliia Manziuk, 2019. "Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality," Papers 1910.13205, arXiv.org.
- Olivier Guéant & Iuliia Manziuk, 2019. "Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03252505, HAL.
- Kai Detlefsen & Giacomo Scandolo, 2005. "Conditional and dynamic convex risk measures," Finance and Stochastics, Springer, vol. 9(4), pages 539-561, October.
- Olivier Guéant & Iuliia Manziuk, 2019. "Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 26(5), pages 387-452, September.
- Sumitra Ganesh & Nelson Vadori & Mengda Xu & Hua Zheng & Prashant Reddy & Manuela Veloso, 2019. "Reinforcement Learning for Market Making in a Multi-agent Dealer Market," Papers 1911.05892, arXiv.org.
- Kai Detlefsen & Giacomo Scandolo, 2005. "Conditional and Dynamic Convex Risk Measures," SFB 649 Discussion Papers SFB649DP2005-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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Cited by:
- Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
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