The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space $mathcal{S}$ and the action space $mathcal{A}$ are both finite, to obtain a nearly optimal policy with sampling access to a generative model, the minimax optimal sample complexity scales linearly with $|mathcal{S}|times|mathcal{A}|$, which can be prohibitively large when $mathcal{S}$ or $mathcal{A}$ is large. This paper considers a Markov decision process (MDP) that admits a set of state-action features, which can linearly express (or approximate) its probability transition kernel. We show that a model-based approach (resp.$~$Q-learning) provably learns an $varepsilon$-optimal policy (resp.$~$Q-function) with high probability as soon as the sample size exceeds the order of $frac{K}{(1-gamma)^{3}varepsilon^{2}}$ (resp.$~$$frac{K}{(1-gamma)^{4}varepsilon^{2}}$), up to some logarithmic factor. Here $K$ is the feature dimension and $gammain(0,1)$ is the discount factor of the MDP. Both sample complexity bounds are provably tight, and our result for the model-based approach matches the minimax lower bound. Our results show that for arbitrarily large-scale MDP, both the model-based approach and Q-learning are sample-efficient when $K$ is relatively small, and hence the title of this paper.
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