We present ContainerGym, a benchmark for reinforcement learning inspired by a
real-world industrial resource allocation task. The proposed benchmark encodes
a range of challenges commonly encountered in real-world sequential decision
making problems, such as uncertainty. It can be configured to instantiate
problems of varying degrees of difficulty, e.g., in terms of variable
dimensionality. Our benchmark differs from other reinforcement learning
benchmarks, including the ones aiming to encode real-world difficulties, in
that it is directly derived from a real-world industrial problem, which
underwent minimal simplification and streamlining. It is sufficiently versatile
to evaluate reinforcement learning algorithms on any real-world problem that
fits our resource allocation framework. We provide results of standard baseline
methods. Going beyond the usual training reward curves, our results and the
statistical tools used to interpret them allow to highlight interesting
limitations of well-known deep reinforcement learning algorithms, namely PPO,
TRPO and DQN.

ContainerGym 是一个受到现实世界工业资源分配任务启发的强化学习基准，可以模拟真实世界顺序决策问题中常遇到的一系列挑战，如不确定性。它可以根据需要进行不同难度的配置，例如变量维度。与其他旨在模拟真实世界困难性的强化学习基准不同的是，我们的基准直接源自一个经过最小化简化和优化的真实工业问题。它足够通用，可以评估适用于我们资源分配框架的任何真实世界问题的强化学习算法。我们提供了标准基线方法的结果。我们的结果及其解释工具超越了通常的训练奖励曲线，可以凸显出众所周知的深度强化学习算法（如 PPO、TRPO 和 DQN）的有趣局限性。

ContainerGym：一个面向资源分配的现实强化学习基准测试

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource  Allocation

Traffic signal control is an important and challenging real-world problem,
which aims to minimize the travel time of vehicles by coordinating their
movements at the road intersections. Current traffic signal control systems in
use still rely heavily on oversimplified information and rule-based methods,
although we now have richer data, more computing power and advanced methods to
drive the development of intelligent transportation. With the growing interest
in intelligent transportation using machine learning methods like reinforcement
learning, this survey covers the widely acknowledged transportation approaches
and a comprehensive list of recent literature on reinforcement for traffic
signal control. We hope this survey can foster interdisciplinary research on
this important topic.

该论文调查了机器学习中的强化学习方法在交通信号控制中的应用，并提出通过综合现有文献促进该重要研究领域的跨学科研究。

交通信号控制方法调查

A Survey on Traffic Signal Control Methods

We present DeblurGAN, an end-to-end learned method for motion deblurring. The
learning is based on a conditional GAN and the content loss . DeblurGAN
achieves state-of-the art performance both in the structural similarity measure
and visual appearance. The quality of the deblurring model is also evaluated in
a novel way on a real-world problem -- object detection on (de-)blurred images.
The method is 5 times faster than the closest competitor -- DeepDeblur. We also
introduce a novel method for generating synthetic motion blurred images from
sharp ones, allowing realistic dataset augmentation.
The model, code and the dataset are available at
this https URL

DeblurGAN 是一种基于条件 GAN 和内容损失学习的运动去模糊的端到端学习方法，能在结构相似度和视觉效果方面达到最先进的性能，同时还能提高检测运动去模糊图像的速度，而且还引入了一种新方法，通过运动去模糊合成运动模糊图像，从而实现数据增强。