Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value
  Expansion   Integrating model-free and model-based approaches in reinforcement learning
has the potential to achieve the high performance of model-free algorithms with
low sample complexity. However, this is difficult because an imperfect dynamics
model can degrade the performance of the learning algorithm, and in
sufficiently complex environments, the dynamics model will almost always be
imperfect. As a result, a key challenge is to combine model-based approaches
with model-free learning in such a way that errors in the model do not degrade
performance. We propose stochastic ensemble value expansion (STEVE), a novel
model-based technique that addresses this issue. By dynamically interpolating
between model rollouts of various horizon lengths for each individual example,
STEVE ensures that the model is only utilized when doing so does not introduce
significant errors. Our approach outperforms model-free baselines on
challenging continuous control benchmarks with an order-of-magnitude increase
in sample efficiency, and in contrast to previous model-based approaches,
performance does not degrade in complex environments.

28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph