Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management

Deep Reinforcement Learning (DRL) approaches to Online Portfolio Selection (OLPS) 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 compare combinations of RL algorithms (DDPG, PPO, A2C, SAC), market representations (Prices, Windows, Indicators) and rewards (Returns and Risk). We found that most DRL algorithms were not robust, with strategies generalizing poorly and degrading quickly during backtesting.