Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 19 Jun 2023.
Builds on
10 of 17 references.
References · 10
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2017Deep Reinforcement Learning that Matterscited by 754
- 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
- 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
- 2019Measuring the Reliability of Reinforcement Learning Algorithmscited by 51
- 2018Deep Reinforcement Learning for Autonomous Drivingcited by 36
- 2020Cost-Sensitive Portfolio Selection via Deep Reinforcement Learningcited by 21
- 2020Bridging the gap between Markowitz planning and deep reinforcement learningcited by 5
- 2022Asset Allocation: From Markowitz to Deep Reinforcement Learningcited by 3
- 2021High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learningcited by 2
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