Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Flow-based Intrinsic Curiosity Module   In this paper, we focus on a prediction-based novelty estimation strategy
upon the deep reinforcement learning (DRL) framework, and present a flow-based
intrinsic curiosity module (FICM) to exploit the prediction errors from optical
flow estimation as exploration bonuses. We propose the concept of leveraging
motion features captured between consecutive observations to evaluate the
novelty of observations in an environment. FICM encourages a DRL agent to
explore observations with unfamiliar motion features, and requires only two
consecutive frames to obtain sufficient information when estimating the
novelty. We evaluate our method and compare it with a number of existing
methods on multiple benchmark environments, including Atari games, Super Mario
Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or
environments featuring moving objects, which allow FICM to utilize the motion
features between consecutive observations. We further ablatively analyze the
encoding efficiency of FICM, and discuss its applicable domains
comprehensively.

20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th