Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Autonomous Reinforcement Learning: Automatic Setting of
  Hyper-parameters using Bayesian Optimization   With the increase of machine learning usage by industries and scientific
communities in a variety of tasks such as text mining, image recognition and
self-driving cars, automatic setting of hyper-parameter in learning algorithms
is a key factor for achieving satisfactory performance regardless of user
expertise in the inner workings of the techniques and methodologies. In
particular, for a reinforcement learning algorithm, the efficiency of an agent
learning a control policy in an uncertain environment is heavily dependent on
the hyper-parameters used to balance exploration with exploitation. In this
work, an autonomous learning framework that integrates Bayesian optimization
with Gaussian process regression to optimize the hyper-parameters of a
reinforcement learning algorithm, is proposed. Also, a bandits-based approach
to achieve a balance between computational costs and decreasing uncertainty
about the Q-values, is presented. A gridworld example is used to highlight how
hyper-parameter configurations of a learning algorithm (SARSA) are iteratively
improved based on two performance functions.

17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th