I've been struggling to find engaging papers on the application of deep reinforcement learning in quantitative risk analysis, portfolio management, algorithmic trading and/or options pricing. What are some papers with interesting findings on this topic that you know of?
You can find a lot of good papers by just typing keywords like "deep reinforcement learning finance" in the arXiv or Google Scholar or looking at top researchers websites which provides an overflow of applications and research directions to engage with. Anyway, here are a few off the top of my head:
If you are looking for a more introductory level paper, this master's thesis applied distributional reinforcement learning to the problem of optimal execution and provides code which is quite rare & nice.
If you want more comprehensive, recent work, I enjoyed "Optimal Execution of Foreign Securities" by Cartea & Arribas which was a unique application of machine learning to optimal trading in the FX market - found here. Rama Cont and Justin Sirgnano wrote a short paper back in 2018 with respect to the problem of detecting price formation using deep learning here. Charles Lehalle also has some interesting work on not so much deep learning, but reinforcement learning you could find on his Google Scholar page. I really liked this paper by Zhang et al (2019) on deep learning to also predict price formation - it was covered in a lot of detail and they include a lot of citations if you want to look around some more. While you asked for deep reinforcement learning, here's an application of q-learning to options pricing (there are many more).
You can also find older, but still interesting papers like this which applies deep reinforcement learning to trading cryptos - warning: littered with typos the last time I read it.
You can check the recent article "Deep Reinforcement Learning for Algorithmic Trading" by Cartea et. al. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3812473