Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Actor-critic versus direct policy search: a comparison based on sample
  complexity   Sample efficiency is a critical property when optimizing policy parameters
for the controller of a robot. In this paper, we evaluate two state-of-the-art
policy optimization algorithms. One is a recent deep reinforcement learning
method based on an actor-critic algorithm, Deep Deterministic Policy Gradient
(DDPG), that has been shown to perform well on various control benchmarks. The
other one is a direct policy search method, Covariance Matrix Adaptation
Evolution Strategy (CMA-ES), a black-box optimization method that is widely
used for robot learning. The algorithms are evaluated on a continuous version
of the mountain car benchmark problem, so as to compare their sample
complexity. From a preliminary analysis, we expect DDPG to be more sample
efficient than CMA-ES, which is confirmed by our experimental results.

5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex