mlsanityData to 14 Oct 2026

Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards

arXiv 2502.02619 ↗First posted 4 Feb 2025q-fin.PM cs.LG q-fin.RMcited by 1

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents. Our target is to enhance the traditional 60/40 benchmark (60% stocks, 40% bonds) by employing the Regret-based Sharpe reward function. To address the impact of transaction fee frictions and prevent signal loss, we develop a transaction cost scheduler. We introduce a future-looking reward function and employ synthetic data training through a circular block bootstrap method to facilitate the learning of generalizable allocation strategies. We focus on two key evaluation measures: return and maximum drawdown. Given the high stochasticity of financial markets, we train 20 independent agents each period and evaluate their average performance against the benchmark. Our method not only enhances the performance of the existing portfolio strategy through strategic rebalancing but also demonstrates strong results compared to other baselines.

Key facts. Cited by 1 paper 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 12 of its 40 references that are in the map. First posted on arXiv 4 Feb 2025.

Builds on

12 of 40 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Smart "Predict, then Optimize"cited by 339
  3. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  4. 2021Recent Advances in Reinforcement Learning in Financecited by 110
  5. 2019Smart Predict-and-Optimize for Hard Combinatorial Optimization Problemscited by 89
  6. 2019Curriculum Learning in Deep Neural Networks for Financial Forecastingcited by 8
  7. 2020Bridging the gap between Markowitz planning and deep reinforcement learningcited by 5
  8. 2020Detecting and adapting to crisis pattern with context based Deep Reinforcement Learningcited by 4
  9. 2022Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendationscited by 3
  10. 2023A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocationcited by 3
  11. 2022Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset tradingcited by 2
  12. 2023Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learningcited by 1

Similar, not cited

Close in wording, no citation either way

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  2. 2026Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization
  3. 2026Deep Reinforcement Learning Framework for Diversified Portfolio Management Across Global Equity Markets
  4. 2024Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework
  5. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
  6. 2024Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
  7. 2026Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
  8. 2024Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization