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 transition kernel



The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model Laixi Shi Caltech Gen Li

Neural Information Processing Systems

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).







Dynamic Regret of Adversarial Linear Mixture MDPs

Neural Information Processing Systems

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.