# Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

arXiv: https://arxiv.org/abs/2312.15385 · HTML: https://mlsanity.com/paper/2312.15385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 24 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (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)
- [Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms](https://mlsanity.com/paper/2111.11232/) (2021)
- [q-Learning in Continuous Time](https://mlsanity.com/paper/2207.00713/) (2022)
- [Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach](https://mlsanity.com/paper/2108.06655/) (2021)
- [Practical Deep Reinforcement Learning Approach for Stock Trading](https://mlsanity.com/paper/1811.07522/) (2018)
- [An intelligent financial portfolio trading strategy using deep Q-learning](https://mlsanity.com/paper/1907.03665/) (2019)
- [Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey](https://mlsanity.com/paper/1904.04973/) (2019)
- [G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning](https://mlsanity.com/paper/2002.10990/) (2020)
- [Large scale continuous-time mean-variance portfolio allocation via reinforcement learning](https://mlsanity.com/paper/1907.11718/) (2019)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application](https://mlsanity.com/paper/2412.05906/) (2024)
- [Multi-period Asset-liability Management with Reinforcement Learning in a Regime-Switching Market](https://mlsanity.com/paper/2509.03251/) (2025)

## Abstract (arXiv)

This paper studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in citezhou2020mv, the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model.