mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization

arXiv 2602.17098 ↗First posted 19 Feb 2026q-fin.PM cs.AI cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio optimization is a key component that involves allocating the portfolio assets so as to maximize returns while minimizing risk taken. It is typically carried out by financial professionals who use a combination of quantitative techniques and investment expertise to make decisions about the portfolio allocation. Recent applications of Deep Reinforcement Learning (DRL) have shown promising results when used to optimize portfolio allocation by training model-free agents on historical market data. Many of these methods compare their results against basic benchmarks or other state-of-the-art DRL agents but often fail to compare their performance against traditional methods used by financial professionals in practical settings. One of the most commonly used methods for this task is Mean-Variance Portfolio Optimization (MVO), which uses historical time series information to estimate expected asset returns and covariances, which are then used to optimize for an investment objective. Our work is a thorough comparison between model-free DRL and MVO for optimal portfolio allocation. We detail the specifics of how to make DRL for portfolio optimization work in practice, also noting the adjustments needed for MVO. Backtest results demonstrate strong performance of the DRL agent across many metrics, including Sharpe ratio, maximum drawdowns, and absolute returns.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 51 references that are in the map. First posted on arXiv 19 Feb 2026.

Builds on

21 of 51 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  5. 2017Deep Reinforcement Learning that Matterscited by 727
  6. 2018Deep Hedgingcited by 106
  7. 2021Recent Advances in Reinforcement Learning in Financecited by 102
  8. 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 86
  9. 2019Deep Reinforcement Learning for Tradingcited by 73
  10. 2020Reinforcement Learning in Economics and Financecited by 57
  11. 2020Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction Statescited by 55
  12. 2019AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networkscited by 39
9 more
  1. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 31
  2. 2020An Application of Deep Reinforcement Learning to Algorithmic Tradingcited by 30
  3. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
  4. 2016On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systemscited by 28
  5. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategycited by 15
  6. 2020How to Make Deep RL Work in Practicecited by 8
  7. 2020Time your hedge with Deep Reinforcement Learningcited by 2
  8. 2021Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approachcited by 2
  9. 2021Robust Portfolio Selection Problems: A Comprehensive Reviewcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalentscited by 0
  2. 2026Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimizationcited by 0