Audio-visual navigation is an audio-targeted wayfinding task where a robot
agent is entailed to travel a never-before-seen 3D environment towards the
sounding source. In this article, we present ORAN, an omnidirectional
audio-visual navigator based on cross-task navigation skill transfer. In
particular, ORAN sharpens its two basic abilities for a such challenging task,
namely wayfinding and audio-visual information gathering. First, ORAN is
trained with a confidence-aware cross-task policy distillation (CCPD) strategy.
CCPD transfers the fundamental, point-to-point wayfinding skill that is well
trained on the large-scale PointGoal task to ORAN, so as to help ORAN to better
master audio-visual navigation with far fewer training samples. To improve the
efficiency of knowledge transfer and address the domain gap, CCPD is made to be
adaptive to the decision confidence of the teacher policy. Second, ORAN is
equipped with an omnidirectional information gathering (OIG) mechanism, i.e.,
gleaning visual-acoustic observations from different directions before
decision-making. As a result, ORAN yields more robust navigation behaviour.
Taking CCPD and OIG together, ORAN significantly outperforms previous
competitors. After the model ensemble, we got 1st in Soundspaces Challenge
2022, improving SPL and SR by 53% and 35% relatively.

基于交叉任务导航技能迁移的全向音频视觉导航器 ORAN，通过信心感知的交叉任务策略提炼（CCPD）和全向信息收集（OIG）的机制，显著超越以往的竞争对手，提高了音空间挑战 2022 年的 SPL 和 SR 等指标。

基于知识转移的全向信息获取的音视频导航

Omnidirectional Information Gathering for Knowledge Transfer-based  Audio-Visual Navigation

Open Radio Access Network (ORAN) is being developed with an aim to
democratise access and lower the cost of future mobile data networks,
supporting network services with various QoS requirements, such as massive IoT
and URLLC. In ORAN, network functionality is dis-aggregated into remote units
(RUs), distributed units (DUs) and central units (CUs), which allows flexible
software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the
mapping of variable RU requirements to local mobile edge computing centres for
future centralized processing would significantly reduce the power consumption
in cellular networks. In this paper, we study the RU-DU resource assignment
problem in an ORAN system, modelled as a 2D bin packing problem. A deep
reinforcement learning-based self-play approach is proposed to achieve
efficient RU-DU resource management, with AlphaGo Zero inspired neural
Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing
environment and real sites data show that the self-play learning strategy
achieves intelligent RU-DU resource assignment for different network
conditions.

本文提出了基于深度强化学习和类似于 AlphaGo Zero 的神经蒙特卡罗树搜索的自我对抗自学习策略，以实现不同网络条件下的智能 RU-DU 资源分配，并研究了 ORAN 系统中 RU-DU 资源分配问题的 2D 装箱模型