Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning

@article{Benhamou2020DetectingAA,
  title={Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning},
  author={E. Benhamou and D. Saltiel and J. Ohana and J. Atif},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.07200}
}
Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as… Expand
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