Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces   Despite the wealth of research into provably efficient reinforcement learning
algorithms, most works focus on tabular representation and thus struggle to
handle exponentially or infinitely large state-action spaces. In this paper, we
consider episodic reinforcement learning with a continuous state-action space
which is assumed to be equipped with a natural metric that characterizes the
proximity between different states and actions. We propose ZoomRL, an online
algorithm that leverages ideas from continuous bandits to learn an adaptive
discretization of the joint space by zooming in more promising and frequently
visited regions while carefully balancing the exploitation-exploration
trade-off. We show that ZoomRL achieves a worst-case regret
$\tilde{O}(H^{\frac{5}{2}} K^{\frac{d+1}{d+2}})$ where $H$ is the planning
horizon, $K$ is the number of episodes and $d$ is the covering dimension of the
space with respect to the metric. Moreover, our algorithm enjoys improved
metric-dependent guarantees that reflect the geometry of the underlying space.
Finally, we show that our algorithm is robust to small misspecification errors.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics