Deep reinforcement learning has achieved remarkable performance in various
domains by leveraging deep neural networks for approximating value functions
and policies. However, using neural networks to approximate value functions or
policy functions still faces challenges, including low sample efficiency and
overfitting. In this paper, we introduce OMNet, a novel learning paradigm
utilizing multiple subnetworks within a single network, offering diverse
outputs efficiently. We provide a systematic pipeline, including
initialization, training, and sampling with OMNet. OMNet can be easily applied
to various deep reinforcement learning algorithms with minimal additional
overhead. Through comprehensive evaluations conducted on MuJoCo benchmark, our
findings highlight OMNet's ability to strike an effective balance between
performance and computational cost.

使用深度强化学习和神经网络对值函数和策略函数进行逼近在各个领域都取得了显著的成果，但是这种方法仍然面临低采样效率和过拟合的挑战，本文介绍了一种名为 OMNet 的新学习范式，在单个网络中利用多个子网络高效地提供多样化输出，我们提供了 OMNet 的初始化、训练和采样的系统流程，它可以轻松应用于各种深度强化学习算法，并且在 MuJoCo 基准测试中的综合评估结果突出了 OMNet 在性能和计算成本之间找到有效平衡的能力。

一个更多：单一网络中的多样观点用于高效深度强化学习

One is More: Diverse Perspectives within a Single Network for Efficient  DRL

Point cloud registration is a key task in many computational fields. Previous
correspondence matching based methods require the inputs to have distinctive
geometric structures to fit a 3D rigid transformation according to point-wise
sparse feature matches. However, the accuracy of transformation heavily relies
on the quality of extracted features, which are prone to errors with respect to
partiality and noise. In addition, they can not utilize the geometric knowledge
of all the overlapping regions. On the other hand, previous global feature
based approaches can utilize the entire point cloud for the registration,
however they ignore the negative effect of non-overlapping points when
aggregating global features. In this paper, we present OMNet, a global feature
based iterative network for partial-to-partial point cloud registration. We
learn overlapping masks to reject non-overlapping regions, which converts the
partial-to-partial registration to the registration of the same shape.
Moreover, the previously used data is sampled only once from the CAD models for
each object, resulting in the same point clouds for the source and reference.
We propose a more practical manner of data generation where a CAD model is
sampled twice for the source and reference, avoiding the previously prevalent
over-fitting issue. Experimental results show that our method achieves
state-of-the-art performance compared to traditional and deep learning based
methods. Code is available at this https URL

本研究提出了一种基于全局特征的 OMNet 方法，用于局部 - 局部点云配准。通过学习重叠区域的掩码来拒绝不重叠的区域，并采用了更实用的数据生成方法，实现了与传统和深度学习方法相比的最新性能。