Building deep reinforcement learning agents that can generalize and adapt to
unseen environments remains a fundamental challenge for AI. This paper
describes progresses on this challenge in the context of man-made environments,
which are visually diverse but contain intrinsic semantic regularities. We
propose a hybrid model-based and model-free approach, LEArning and Planning
with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on
visual inputs, and a Bayesian model over semantic structures. When placed in an
unseen environment, the agent plans with the semantic model to make high-level
decisions, proposes the next sub-target for the sub-policy to execute, and
updates the semantic model based on new observations. We perform experiments in
visual navigation tasks using House3D, a 3D environment that contains diverse
human-designed indoor scenes with real-world objects. LEAPS outperforms strong
baselines that do not explicitly plan using the semantic content.

本论文提出了一种基于深度强化学习的混合模型与无模型集成的方法 LEAPS，通过对含有内在语义规律但视觉多样性的人造环境的实验表明其比不考虑语义内容的强基线更有效。

使用语义模型进行学习和规划

Learning and Planning with a Semantic Model

We propose a novel method for detecting horizontal vanishing points and the
zenith vanishing point in man-made environments. The dominant trend in existing
methods is to first find candidate vanishing points, then remove outliers by
enforcing mutual orthogonality. Our method reverses this process: we propose a
set of horizon line candidates and score each based on the vanishing points it
contains. A key element of our approach is the use of global image context,
extracted with a deep convolutional network, to constrain the set of candidates
under consideration. Our method does not make a Manhattan-world assumption and
can operate effectively on scenes with only a single horizontal vanishing
point. We evaluate our approach on three benchmark datasets and achieve
state-of-the-art performance on each. In addition, our approach is
significantly faster than the previous best method.

本文提出了一种在人工环境中检测水平消失点和天顶消失点的新方法，利用深度卷积网络提取全局图像上下文，并逆转现有方法的先后顺序，通过提出一组地平线候选者并针对其包含的消失点对其进行打分，而非先找到候选消失点再通过强制互相正交来消除异常值。我们在三个基准数据集上评估了我们的方法，并在每个数据集上均取得了最好的性能，同时我们的方法明显快于之前最好的方法。