TAG ARCHIVE
reinforcement-learning
2 MARIA OS blog articles tagged reinforcement-learning, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Recursive Adaptation in Action Routing: How MARIA OS Routes Learn from Execution Outcomes
How self-improving routing uses recursive execution feedback to converge toward high-quality policies while preserving Lyapunov stability guarantees
Static action routing — where rules are configured once and applied uniformly — is inadequate for enterprise AI governance. Agent capabilities evolve, workloads shift, and routing quality depends on context that is only observed after execution. This paper introduces a recursive adaptation framework for MARIA OS action routing in which execution outcomes update routing parameters through a formal learning rule. We define θ_{t+1} = θ_t + η∇J(θ_t), where J(θ) is expected routing quality and gradients are estimated from outcome signals. We prove convergence under standard stochastic-approximation assumptions and establish Lyapunov stability guarantees, showing the adaptation process remains bounded while converging toward locally optimal routing policies. Thompson sampling provides principled exploration, and a multi-agent coordination protocol prevents oscillatory conflicts under concurrent adaptation. The quantitative figures in this article should be read as replay and simulation outputs over 14 operating contexts, not as audited production metrics of the current shipping router.
Actor-Critic Reinforcement Learning for Gated Autonomy: PPO-Based Policy Optimization Under Responsibility Constraints
How Proximal Policy Optimization enables medium-risk task automation while respecting human approval gates
Gated autonomy requires reinforcement learning that respects responsibility boundaries. This paper positions actor-critic methods — specifically PPO — as a core algorithm in the Control Layer, showing how the actor learns policies, the critic estimates state value, and responsibility gates constrain the action space dynamically. We derive a gate-constrained policy-gradient formulation, analyze PPO clipping behavior under trust-region constraints, and model human-in-the-loop approval as part of environment dynamics.