Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is Q-learning Provably Efficient?   Model-free reinforcement learning (RL) algorithms, such as Q-learning,
directly parameterize and update value functions or policies without explicitly
modeling the environment. They are typically simpler, more flexible to use, and
thus more prevalent in modern deep RL than model-based approaches. However,
empirical work has suggested that model-free algorithms may require more
samples to learn [Deisenroth and Rasmussen 2011, Schulman et al. 2015]. The
theoretical question of "whether model-free algorithms can be made sample
efficient" is one of the most fundamental questions in RL, and remains unsolved
even in the basic scenario with finitely many states and actions.
  We prove that, in an episodic MDP setting, Q-learning with UCB exploration
achieves regret $\tilde{O}(\sqrt{H^3 SAT})$, where $S$ and $A$ are the numbers
of states and actions, $H$ is the number of steps per episode, and $T$ is the
total number of steps. This sample efficiency matches the optimal regret that
can be achieved by any model-based approach, up to a single $\sqrt{H}$ factor.
To the best of our knowledge, this is the first analysis in the model-free
setting that establishes $\sqrt{T}$ regret without requiring access to a
"simulator."

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The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: math-ph