Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fractional Moments on Bandit Problems   Reinforcement learning addresses the dilemma between exploration to find
profitable actions and exploitation to act according to the best observations
already made. Bandit problems are one such class of problems in stateless
environments that represent this explore/exploit situation. We propose a
learning algorithm for bandit problems based on fractional expectation of
rewards acquired. The algorithm is theoretically shown to converge on an
eta-optimal arm and achieve O(n) sample complexity. Experimental results show
the algorithm incurs substantially lower regrets than parameter-optimized
eta-greedy and SoftMax approaches and other low sample complexity
state-of-the-art techniques.

18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an