First, note that we shall use the same MDPs defined in Appendix D.1 as follows

In this paper, we are particularly interested in understanding whether, and how, the choice of distributional robustness bears statistical implications in learning the desired policy, by studying the sample complexity in the widely-used generative model (Kearns and Singh, 1999).

This gives rise to the RTK-MALA and RTK-ULD algorithms for diffusion inference.

In contrast, we design a discrete-time algorithm and derive its convergence rate solely under weakly monotone conditions.

Typically, deep RL algorithms learn a policy in an online trial-and-error fashion using millions to billions of data.