Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Smooth and Efficient Policy Exploration for Robot Trajectory Learning   Many policy search algorithms have been proposed for robot learning and
proved to be practical in real robot applications. However, there are still
hyperparameters in the algorithms, such as the exploration rate, which requires
manual tuning. The existing methods to design the exploration rate manually or
automatically may not be general enough or hard to apply in the real robot. In
this paper, we propose a learning model to update the exploration rate
adaptively. The overall algorithm is a combination of methods proposed by other
researchers. Smooth trajectories for the robot can be produced by the algorithm
and the updated exploration rate maximizes the lower bound of the expected
return. Our method is tested in the ball-in-cup problem. The results show that
our method can receive the same learning outcome as the previous methods but
with fewer iterations.

6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th