Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging Imagination and Reality for Model-Based Deep Reinforcement
  Learning   Sample efficiency has been one of the major challenges for deep reinforcement
learning. Recently, model-based reinforcement learning has been proposed to
address this challenge by performing planning on imaginary trajectories with a
learned world model. However, world model learning may suffer from overfitting
to training trajectories, and thus model-based value estimation and policy
search will be pone to be sucked in an inferior local policy. In this paper, we
propose a novel model-based reinforcement learning algorithm, called BrIdging
Reality and Dream (BIRD). It maximizes the mutual information between imaginary
and real trajectories so that the policy improvement learned from imaginary
trajectories can be easily generalized to real trajectories. We demonstrate
that our approach improves sample efficiency of model-based planning, and
achieves state-of-the-art performance on challenging visual control benchmarks.

30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con