# Bridging the gap between Markowitz planning and deep reinforcement learning

arXiv: https://arxiv.org/abs/2010.09108 · HTML: https://mlsanity.com/paper/2010.09108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting](https://mlsanity.com/paper/2104.10483/) (2021)
- [Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management](https://mlsanity.com/paper/2306.10950/) (2023)
- [Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards](https://mlsanity.com/paper/2502.02619/) (2025)

## Abstract (arXiv)

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.