Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Effective Scheduling of Model-based Reinforcement Learning   Model-based reinforcement learning has attracted wide attention due to its
superior sample efficiency. Despite its impressive success so far, it is still
unclear how to appropriately schedule the important hyperparameters to achieve
adequate performance, such as the real data ratio for policy optimization in
Dyna-style model-based algorithms. In this paper, we first theoretically
analyze the role of real data in policy training, which suggests that gradually
increasing the ratio of real data yields better performance. Inspired by the
analysis, we propose a framework named AutoMBPO to automatically schedule the
real data ratio as well as other hyperparameters in training model-based policy
optimization (MBPO) algorithm, a representative running case of model-based
methods. On several continuous control tasks, the MBPO instance trained with
hyperparameters scheduled by AutoMBPO can significantly surpass the original
one, and the real data ratio schedule found by AutoMBPO shows consistency with
our theoretical analysis.

0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th