Reinforcement Learning-Based Autonomous Decision Making with Large Language Models
Keywords:
reinforcement learning, large language models, autonomous decision making, socio-technical systems, governance, robustness, fairness, deployment sustainabilityAbstract
The integration of reinforcement learning with large language models has created new possibilities for autonomous decision making in complex, uncertain, and socio-technical environments. Reinforcement learning provides mechanisms for sequential optimization through reward-driven interaction, while large language models contribute broad semantic knowledge, flexible reasoning, and natural-language interfaces. When combined, these technologies can support agents that interpret goals, plan across long horizons, invoke external tools, negotiate with human stakeholders, and adapt to changing constraints. This paper examines the system-level implications of reinforcement learning-based autonomous decision making with large language models. It emphasizes architectural patterns, governance layers, infrastructure requirements, deployment sustainability, robustness under uncertainty, fairness, accountability, and policy consequences. The analysis suggests that the primary challenge is not merely improving model capability but designing accountable socio-technical systems in which language-based reasoning, reinforcement learning, human oversight, and institutional controls are mutually reinforcing. Key risks include reward misspecification, opaque decision pathways, hidden technical debt, uneven access to computational resources, and weak mechanisms for contestability. The paper argues that robust autonomous decision making requires hybrid architectures that separate semantic reasoning from safety-critical control, incorporate continuous evaluation, preserve human authority, and treat governance as an operational component rather than an afterthought. Cross-domain illustrations from healthcare, mobility, finance, and public administration demonstrate that context determines acceptable autonomy. The conclusion recommends layered assurance, transparent documentation, federated and energy-aware deployment, and regulatory frameworks that remain adaptive without becoming permissive. Such an approach can harness the complementary strengths of reinforcement learning and large language models while constraining their failure modes.
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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.