Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

集体决策对于最近的信息和通信技术至关重要。在我们以前的研究中，我们在数学上得出了无冲突的联合决策，最佳地满足了玩家的概率偏好概况。但是，关于最佳联合决策方法存在两个问题。首先，随着选择的数量的增加，计算最佳关节选择概率矩阵爆炸的计算成本。其次，要得出最佳的关节选择概率矩阵，所有玩家都必须披露其概率偏好。现在，值得注意的是，不一定需要对关节概率分布的明确计算；集体决策的必要条件是抽样。这项研究研究了几种抽样方法，这些方法会融合到满足玩家偏好的启发式关节选择概率矩阵。我们表明，它们可以大大减少上述计算成本和机密性问题。我们分析了每种采样方法的概率分布，以及所需的计算成本和保密性。特别是，我们通过光子的量子干扰引入了两种无冲突的关节抽样方法。第一个系统允许玩家隐藏自己的选择，同时在玩家具有相同的偏好时几乎完美地满足了玩家的喜好。第二个系统，其物理性质取代了昂贵的计算成本，它也掩盖了他们的选择，因为他们拥有可信赖的第三方。

Microswimmers can acquire information on the surrounding fluid by sensing mechanical queues. They can then navigate in response to these signals. We analyse this navigation by combining deep reinforcement learning with direct numerical simulations to resolve the hydrodynamics. We study how local and non-local information can be used to train a swimmer to achieve particular swimming tasks in a non-uniform flow field, in particular a zig-zag shear flow. The swimming tasks are (1) learning how to swim in the vorticity direction, (2) the shear-gradient direction, and (3) the shear flow direction. We find that access to lab frame information on the swimmer's instantaneous orientation is all that is required in order to reach the optimal policy for (1,2). However, information on both the translational and rotational velocities seem to be required to achieve (3). Inspired by biological microorganisms we also consider the case where the swimmers sense local information, i.e. surface hydrodynamic forces, together with a signal direction. This might correspond to gravity or, for micro-organisms with light sensors, a light source. In this case, we show that the swimmer can reach a comparable level of performance as a swimmer with access to lab frame variables. We also analyse the role of different swimming modes, i.e. pusher, puller, and neutral swimmers.

The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena. The difficulty of imaging fast moving scenes lies in a trade-off between motion blur and underexposure noise: On the one hand, recordings with long exposure times suffer from motion blur effects caused by movements in the recorded scene. On the other hand, the amount of light reaching camera photosensors decreases with exposure times so that short-exposure recordings suffer from underexposure noise. In this paper, we propose to address this trade-off by treating the problem of high-speed imaging as an underexposed image denoising problem. We combine recent advances on underexposed image denoising using deep learning and adapt these methods to the specificity of the high-speed imaging problem. Leveraging large external datasets with a sensor-specific noise model, our method is able to speedup the acquisition rate of a High-Speed Camera over one order of magnitude while maintaining similar image quality.

使用三维（3D）图像传感器的智能监视一直在智能城市的背景下引起人们的注意。在智能监控中，实施了3D图像传感器获取的点云数据的对象检测，以检测移动物体（例如车辆和行人）以确保道路上的安全性。但是，由于光检测和范围（LIDAR）单元用作3D图像传感器或3D图像传感器的安装位置，因此点云数据的特征是多元化的。尽管迄今已研究了从点云数据进行对象检测的各种深度学习（DL）模型，但尚无研究考虑如何根据点云数据的功能使用多个DL模型。在这项工作中，我们提出了一个基于功能的模型选择框架，该框架通过使用多种DL方法并利用两种人工技术生成的伪不完整的训练数据来创建各种DL模型：采样和噪声添加。它根据在真实环境中获取的点云数据的功能，为对象检测任务选择最合适的DL模型。为了证明提出的框架的有效性，我们使用从KITTI数据集创建的基准数据集比较了多个DL模型的性能，并比较了通过真实室外实验获得的对象检测的示例结果。根据情况，DL模型之间的检测准确性高达32％，这证实了根据情况选择适当的DL模型的重要性。

最近的作品表明，隐式神经表示（INR）具有信号导数的有意义表示的能力。在这项工作中，我们利用该属性来执行视频框架插值（VFI），通过明确限制INR的衍生物以满足光流约束方程。我们仅使用目标视频及其光流，在有限的运动范围内实现了最先进的VFI，而无需从其他培训数据中学习插值操作员。我们进一步表明，限制INR衍生物不仅可以更好地插值中间框架，还可以提高狭窄网络适合观察到的帧的能力，这暗示了潜在的视频压缩和INR优化的应用。