March 14, 2024, 4:42 a.m. | Philip Ndikum, Serge Ndikum

cs.LG updates on

arXiv:2403.07916v1 Announce Type: cross
Abstract: This research paper delves into the application of Deep Reinforcement Learning (DRL) in asset-class agnostic portfolio optimization, integrating industry-grade methodologies with quantitative finance. At the heart of this integration is our robust framework that not only merges advanced DRL algorithms with modern computational techniques but also emphasizes stringent statistical analysis, software engineering and regulatory compliance. To the best of our knowledge, this is the first study integrating financial Reinforcement Learning with sim-to-real methodologies from robotics …

abstract advanced algorithms application arxiv class computational cs.lg finance framework frontiers industry integration investment modern optimization paper portfolio quantitative reinforcement reinforcement learning research research paper robust type

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