Reinforcement Learning Techniques for Artificial General Intelligence under Embedded Ethical and Regulatory Constraints in Censored Data Environments

The integration of reinforcement learning (RL) into Artificial General Intelligence (AGI) presents both a technological opportunity and an ethical challenge. While RL excels in dynamic decision-making, its traditional dependence on clean, unbiased feedback signals renders it vulnerable in real-world environments characterized by data censorship, regulatory constraints, and ambiguous user intent. This paper proposes a modular, multi layered conceptual framework for developing RL-based AGI systems that are ethically aligned, regulation compliant, and robust to informational suppression. The architecture incorporates constrained inverse reinforcement learning (CIRL), ethical policy filters, censorship detection mechanisms, and intention-aware proxy modeling, all governed through a dynamic oversight layer compatible with legal and institutional frameworks. Validation through structured thought experiments demonstrates the framework’s adaptability across critical domains such as healthcare, law, and geopolitics. By aligning with key international standards including the IEEE Ethically Aligned Design, the OECD AI Principles, and the EU AI Act this research offers a foundational blueprint for building transparent, fair, and trustworthy AGI systems in complex socio-technical landscapes.