Conditional Importance Sampling for Off-Policy Learning
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:45-55, 2020.
The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This framework yields new perspectives and understanding of existing off-policy algorithms, and reveals a broad space of unexplored algorithms. We theoretically analyse this space, and concretely investigate several algorithms that arise from this framework.