Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning walk and trot from the same objective using different types of
  exploration   In quadruped gait learning, policy search methods that scale high dimensional
continuous action spaces are commonly used. In most approaches, it is necessary
to introduce prior knowledge on the gaits to limit the highly non-convex search
space of the policies. In this work, we propose a new approach to encode the
symmetry properties of the desired gaits, on the initial covariance of the
Gaussian search distribution, allowing for strategic exploration. Using
episode-based likelihood ratio policy gradient and relative entropy policy
search, we learned the gaits walk and trot on a simulated quadruped. Comparing
these gaits to random gaits learned by initialized diagonal covariance matrix,
we show that the performance can be significantly enhanced.

35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th