Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning in a Handful of Trials using Probabilistic
  Dynamics Models   Model-based reinforcement learning (RL) algorithms can attain excellent
sample efficiency, but often lag behind the best model-free algorithms in terms
of asymptotic performance. This is especially true with high-capacity
parametric function approximators, such as deep networks. In this paper, we
study how to bridge this gap, by employing uncertainty-aware dynamics models.
We propose a new algorithm called probabilistic ensembles with trajectory
sampling (PETS) that combines uncertainty-aware deep network dynamics models
with sampling-based uncertainty propagation. Our comparison to state-of-the-art
model-based and model-free deep RL algorithms shows that our approach matches
the asymptotic performance of model-free algorithms on several challenging
benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125
times fewer samples than Soft Actor Critic and Proximal Policy Optimization
respectively on the half-cheetah task).

24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an