Here's a detailed technical proposal following your guidelines, fulfilling the request for a 10,000+ character research paper targeting the randomized sub-field of AI 시스템의 테스트 및 검증, specifically focusing on automated anomaly detection in reinforcement learning environments. 1. Abstract This paper introduces a novel framework for automated anomaly detection during reinforcement learning (RL) training using spectral decomposition of state-action value function trajectories. Traditional anomaly detection in RL often relies on manual monitoring of reward signals or pre-defined metrics. Our approach, Spectral Anomaly Detection in Reinforcement Learning (SADRL), leverages the inherent structure within RL value functions to identify deviations from normative behavior without requiring explicit anomaly definitions. SADRL dynamically constructs a time series from the agent's Q-values over training episodes and applies spectral analysis to identify unexpected rhythmic patterns indicative of anomalous events, such as environment shifts, agent bugs, or hyperparameter misconfigurations. The method demonstrates superior performance across various simulated RL environments, exhibiting capabilities to flag anomalies with high accuracy and minimal false positives. We present a detailed mathematical formulation, experimental methodology, and demonstrate the framework's immediate commercializability. 2. Introduction Reinforcement learning is increasingly deployed in critical applications ranging from autonomous robotics to financial trading. The reliance on complex, often black-box RL agents necessitates robust monitoring and validation procedures to ensure reliable operation. Unexpected behavior during training – anomalies – can severely degrade performance or lead to unsafe actions once deployed. Existing anomaly detection techniques often require human experts to manually configure monitors, identify specific failure modes, or rely on external sensors,...