: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While … real-time systems. In contrast, the DQN reinforcement learning model continuously learns from every transaction …-term improvement and operational scalability. This work highlights reinforcement learning as a highly promising paradigm for dynamic …

.504-0.641) versus damping in Asian countries. A Reinforcement Learning (RL) agent was trained using Deep Q-Networks with experience …

continuously monitored to guide system optimisation. A reinforcement learning (RL)-based approach is proposed to determine the …

reinforcement learning-based CVaR policies. Performance is evaluated using mean return, volatility, CVaR at multiple confidence …, reinforcement learning-based CVaR strategies adapt better to extreme tails, and regime-based CVaR optimization consistently limits …

, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes …

integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified … flexibility and adaptability of reinforcement learning. Empirical evaluations on U.S. sector ETFs and Dow Jones Industrial Average … that integrating explainable predictive signals with risk-sensitive reinforcement learning improves the robustness and …

operational capacity. Methods: To address these challenges, this study develops a centralized Hierarchical Reinforcement Learning …