Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explicit Explore-Exploit Algorithms in Continuous State Spaces   We present a new model-based algorithm for reinforcement learning (RL) which
consists of explicit exploration and exploitation phases, and is applicable in
large or infinite state spaces. The algorithm maintains a set of dynamics
models consistent with current experience and explores by finding policies
which induce high disagreement between their state predictions. It then
exploits using the refined set of models or experience gathered during
exploration. We show that under realizability and optimal planning assumptions,
our algorithm provably finds a near-optimal policy with a number of samples
that is polynomial in a structural complexity measure which we show to be low
in several natural settings. We then give a practical approximation using
neural networks and demonstrate its performance and sample efficiency in
practice.

26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin