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).

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.

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We study reinforcement learning in episodic inhomogeneous MDPs with adversarial full-information rewards and the unknown transition kernel. We consider the linear mixture MDPs whose transition kernel is a linear mixture model and choose the dynamic regret as the performance measure.

In this paper, departing from the existing literature on IL for MFGs, we introduce a new solution concept called the Nash imitation gap.