mlsanityData to 14 Oct 2026

Financial Decision Making using Reinforcement Learning with Dirichlet Priors and Quantum-Inspired Genetic Optimization

arXiv 2509.00095 ↗First posted 27 Aug 2025cs.LG cs.NEcited by 0

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

Abstract (arXiv)

Traditional budget allocation models struggle with the stochastic and nonlinear nature of real-world financial data. This study proposes a hybrid reinforcement learning (RL) framework for dynamic budget allocation, enhanced with Dirichlet-inspired stochasticity and quantum mutation-based genetic optimization. Using Apple Inc. quarterly financial data (2009 to 2025), the RL agent learns to allocate budgets between Research and Development and Selling, General and Administrative to maximize profitability while adhering to historical spending patterns, with L2 penalties discouraging unrealistic deviations. A Dirichlet distribution governs state evolution to simulate shifting financial contexts. To escape local minima and improve generalization, the trained policy is refined using genetic algorithms with quantum mutation via parameterized qubit rotation circuits. Generation-wise rewards and penalties are logged to visualize convergence and policy behavior. On unseen fiscal data, the model achieves high alignment with actual allocations (cosine similarity 0.9990, KL divergence 0.0023), demonstrating the promise of combining deep RL, stochastic modeling, and quantum-inspired heuristics for adaptive enterprise budgeting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 36 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

13 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Review of Reinforcement Learning in Financial Applications1 Nov
  2. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey20 Aug

Other references · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2016OpenAI Gymcited by 1,736
  5. 2014Convex Optimization: Algorithms and Complexitycited by 575
  6. 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 45
  7. 2023Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunitiescited by 8
  8. 2023Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulationcited by 2
  9. 2023A Budget-Adaptive Allocation Rule for Optimal Computing Budget Allocationcited by 2
  10. 2024Generalized Bayesian deep reinforcement learningcited by 2
  11. 2023Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learningcited by 1

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