# Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization

arXiv: https://arxiv.org/abs/2403.16667 · HTML: https://mlsanity.com/paper/2403.16667/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance](https://mlsanity.com/paper/2011.09607/) (2020)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)
- [Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization](https://mlsanity.com/paper/1901.08740/) (2019)

## Built on by (most cited first, 4 of 4)

- [Decision by Supervised Learning with Deep Ensembles: A Practical Framework for Robust Portfolio Optimization](https://mlsanity.com/paper/2503.13544/) (2025)
- [Statistical Arbitrage in Rank Space](https://mlsanity.com/paper/2410.06568/) (2024)
- [Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization](https://mlsanity.com/paper/2605.09310/) (2026)
- [Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization](https://mlsanity.com/paper/2609.02677/) (2026)

## Abstract (arXiv)

Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance optimization is used with the aim of maximizing returns while minimizing risk, however, more recently, deep reinforcement learning formulations have been explored. Increasingly, investors have demonstrated an interest in incorporating ESG objectives when making investment decisions, and modifications to the classical mean-variance optimization framework have been developed. In this work, we study the use of deep reinforcement learning for responsible portfolio optimization, by incorporating ESG states and objectives, and provide comparisons against modified mean-variance approaches. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation across additive and multiplicative utility functions of financial and ESG responsibility objectives.