Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Model-Based and Model-Free Updates for Trajectory-Centric
  Reinforcement Learning   Reinforcement learning (RL) algorithms for real-world robotic applications
need a data-efficient learning process and the ability to handle complex,
unknown dynamical systems. These requirements are handled well by model-based
and model-free RL approaches, respectively. In this work, we aim to combine the
advantages of these two types of methods in a principled manner. By focusing on
time-varying linear-Gaussian policies, we enable a model-based algorithm based
on the linear quadratic regulator (LQR) that can be integrated into the
model-free framework of path integral policy improvement (PI2). We can further
combine our method with guided policy search (GPS) to train arbitrary
parameterized policies such as deep neural networks. Our simulation and
real-world experiments demonstrate that this method can solve challenging
manipulation tasks with comparable or better performance than model-free
methods while maintaining the sample efficiency of model-based methods. A video
presenting our results is available at
https://sites.google.com/site/icml17pilqr

17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg