Off-Policy Correction Importance Sampling: Utilizing Ratio Weighting to Enable Data Reuse from Non-Target Behavior Policies in Q-Learning Frameworks
Reinforcement learning systems often rely on large volumes of interaction data to learn effective decision-making policies. However, collecting fresh data directly from the target policy can be expensive, slow, or risky in real-world environments. This challenge has led to the widespread adoption of off-policy learning, where historical data generated by a different behaviour policy is…
