强烈的引力透镜已成为一种有前途的方法，用于探测亚半乳尺度上的暗物质模型。最近的工作提出了Subhalo有效密度斜率比常用的Subhalo质量功能更可靠。 subhalo有效密度斜率是一个独立于对基础密度曲线的假设的测量值，可以通过传统的采样方法来推断单个Subhalos。为了超越单个Subhalo测量，我们利用机器学习的最新进展，并引入神经似然比估计器来推断Subhalos人群的有效密度斜率。我们证明我们的方法能够利用多个Subhalos（内部和跨多个图像）的统计能力来区分不同Subhalo种群的特征。神经似然比估计量对传统抽样的估计值所需的计算效率可以实现对暗物质遗传的统计研究，并且特别有用，因为我们希望从即将进行的调查中涌入强镜头系统。

在排放限制下优化的气体网络规划优化优先考虑最少$ _2 $强度的天然气供应。由于此问题包括复杂的气流物理定律，因此标准优化求解器无法保证融合与可行解决方案。为了解决这个问题，我们开发了一个输入 - 控制神经网络（ICNN）辅助优化例程，该程序结合了一组训练有素的ICNN，以高精度近似于气流方程。比利时气体网络上的数值测试表明，ICNN辅助优化主导了非凸和基于弛豫的求解器，其最佳增长较大，与更严格的发射目标有关。此外，每当非凸线求解器失败时，ICNN ADED优化为网络计划提供了可行的解决方案。

Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package. The benchmarks we provide are designed to evaluate different aspects of the continual RL challenge, such as catastrophic forgetting, plasticity, ability to generalize, and sample-efficient learning. Three of the benchmarks utilize video game environments (Atari, Procgen, NetHack). The fourth benchmark, CHORES, consists of four different task sequences in a visually realistic home simulator, drawn from a diverse set of task and scene parameters. To compare continual RL methods on these benchmarks, we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting, and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant, open-source baselines of existing algorithms for researchers to use and expand on. We release CORA and hope that the continual RL community can benefit from our contributions, to accelerate the development of new continual RL algorithms.