Reinforcement Learning Engine for Adaptive Influence Optimization

原文（中文）

The Reinforcement Learning Engine (RLE) optimizes influence scoring in digital ecosystems by aggregating dynamic metrics (e.g., engagement rates, trust scores), applying Proximal Policy Optimization (PPO)-based reinforcement learning with tailored reward functions, adjusting parameters via real-time behavioral feedback, generating optimized influence scores, and delivering secure JSON outputs via an API. The system includes a metric aggregation module, reinforcement learning processor, interaction adjustment unit, influence optimizer with audit logging, and secure output interface. The method ingests metrics, learns from multi-agent interactions, tunes parameters, optimizes scores, and ensures GDPR-compliant, privacy-preserving operations with immutable audit trails. Applications include decentralized governance and reputation management in distributed networks, overcoming limitations of static scoring systems.