Adaptive Program Governance Platform using Reinforcement Learning and Policy Optimization
Year: 2026
Type: conference-paper
Abstract: Effective program governance is essential to align corporate programs with strategic objectives, regulatory mandates, and organizational performance targets. Conventional governance methods are predominantly rule-based and depend on human decision-making, making them inadequate in dynamic contexts characterized by evolving corporate objectives, resource limitations, and fluctuating threats. This research introduces an Adaptive Program Governance Platform (APGP) that integrates Reinforcement Learning (RL) with Policy Optimization for intelligent, data-driven governance decisions. The platform proposes addressing the issue of sequential decision-making in governance by employing a Markov Decision Process (MDP), wherein a reinforcement learning agent continuously acquires optimal governance strategies through interaction with the organizational environment. Consistent policy updates, efficient resource allocation, proactive risk reduction, and improved compliance management are all outcomes of our use of the Proximal Policy Optimization (PPO) approach. The framework consistently updates governance laws using real-time operational data, resulting in faster and more accurate choices while reducing administrative burdens. The experimental evaluation indicates that the proposed platform surpasses conventional rule-based governance models for governance efficiency, policy compliance, resource utilization, schedule adherence, and risk mitigation. The proposed method offers a scalable and astute governance solution for managing extensive commercial efforts in rapidly evolving organizational environments.
Institutions: Westcliff University, Cisco Systems (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203448664