Benchmarking Robustness of Deep Reinforcement Learning approaches to
  Online Portfolio Management
Marc Velay Bich-Liên Doan Arpad Rimmel Fabrice Popineau Fabrice Daniel
Deep Reinforcement Learning approaches to Online Portfolio Selection have
grown in popularity in recent years. The sensitive nature of training
Reinforcement Learning agents implies a need for extensive efforts in market
representation, behavior objectives, and training processes, which have often
been lacking in previous works. We propose a training and evaluation process to
assess the performance of classical DRL algorithms for portfolio management. We
found that most Deep Reinforcement Learning algorithms were not robust, with
strategies generalizing poorly and degrading quickly during backtesting.