On the Optimality of Sparse Model-Based Planning for Markov Decision Processes

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MOReL : Model-Based Offline Reinforcement Learning (2020, methodology): “More recent work suggests that model-based methods are minimax optimal for tabular MDPs when equipped with a wide restart state distribution [58].”
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction (2021, background): “, 2019) or the generative model (simulator) setting (Azar et al., 2013; Sidford et al., 2018a,b; Yang & Wang, 2019; Agarwal et al., 2019; Wainwright, 2019; Lattimore & Szepesvari, 2019), both of which assuming interactive access to the environment and not applicable to offline RL.”
A Game Theoretic Framework for Model Based Reinforcement Learning (2020, background): “To obtain a globally accurate model, we need the ability to collect data from all parts of the state space (Agarwal et al., 2019b), which is often impossible.”
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? (2020, background): “Results in this setting include Kearns and Singh [1999], Kakade [2003], Singh and Yee [1994], Azar et al. [2013], Sidford et al. [2018b,a], Agarwal et al. [2019].”