Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DRIBO: Robust Deep Reinforcement Learning via Multi-View Information
  Bottleneck   Deep reinforcement learning (DRL) agents are often sensitive to visual
changes that were unseen in their training environments. To address this
problem, we leverage the sequential nature of RL to learn robust
representations that encode only task-relevant information from observations
based on the unsupervised multi-view setting. Specifically, we introduce an
auxiliary objective based on the multi-view in-formation bottleneck (MIB)
principle which quantifies the amount of task-irrelevant information and
encourages learning representations that are both predictive of the future and
less sensitive to task-irrelevant distractions. This enables us to train
high-performance policies that are robust to visual distractions and can
generalize to unseen environments. We demonstrate that our approach can achieve
SOTA performance on diverse visual control tasks on the DeepMind Control Suite,
even when the background is replaced with natural videos. In addition, we show
that our approach outperforms well-established baselines for generalization to
unseen environments on the Procgen benchmark. Our code is open-sourced and
available at https://github.com/JmfanBU/DRIBO.

25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat