Conditional Importance Sampling for Off-Policy Learning

This work addresses the challenge of improving off-policy learning methods for reinforcement learning practitioners, though it appears incremental as it builds on existing importance sampling techniques.

The paper tackles the problem of off-policy reinforcement learning by introducing a conceptual framework based on conditional expectations of importance sampling ratios, which provides new insights into existing algorithms and reveals a broad space of unexplored algorithms.