Optimizing Portfolio Allocation Using Reinforcement Learning Techniques
Keywords:
Reinforcement Learning, Portfolio Optimization, Systems Engineering, Financial Infrastructure, Algorithmic Governance, Socio-Technical SystemsAbstract
The rapid evolution of computational finance has shifted the paradigm of asset management from static, rule-based frameworks toward dynamic, autonomous systems capable of navigating non-stationary market environments. This paper investigates the systematic optimization of portfolio allocation through Reinforcement Learning (RL) techniques, emphasizing the architectural, structural, and socio-technical dimensions of large-scale deployment. Unlike traditional mean-variance optimization which relies on historical statistical distributions and periodic rebalancing, RL-based frameworks treat portfolio management as a continuous control problem, enabling agents to learn optimal policies through iterative interaction with global market dynamics. We provide a comprehensive analysis of the infrastructure required to support these high-compute systems, the policy implications of algorithmic convergence, and the critical trade-offs between system robustness and environmental sustainability. Furthermore, the study explores the governance frameworks necessary to address algorithmic fairness and the potential for systemic risk propagation in automated financial ecosystems. By synthesizing perspectives from systems engineering, behavioral finance, and infrastructure management, this research provides a holistic roadmap for the design and implementation of resilient, ethically grounded, and scalable RL-based investment systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
