整数程序为代表广泛的真实计划问题提供了强大的抽象。尽管他们能够模拟一般调度问题，但解决大规模整数程序（IP）在实践中仍然是计算挑战。纳入更复杂的目标，例如鲁棒性对中断进一步加剧了计算挑战。我们呈现出良好的（神经网络IP系数提取），这是一种新颖的技术，它结合了增强学习和整数编程来解决鲁棒调度问题。更具体地说，尼斯使用加强学习在整数编程配方中大致代表复杂的目标。我们很高兴确定飞行员的分配到飞行机组计划，以减少中断的影响。我们将很好的比较（1）基线整数编程配方产生了可行的工作人员计划，（2）强大的整数编程配方，明确尝试最小化中断的影响。我们的实验表明，在各种场景中，良好的生成时间表导致33 \％至48 \％的中断比基线配方更少。此外，在更严重限制的调度方案中，强大的整数程序未能在90分钟内产生时间表，很好能够平均在不到2秒内构建强大的计划。

在机器人，游戏和许多其他地区，加固学习导致各种区域导致相当大的突破。但是在复杂的真实决策中申请RL仍然有限。运营管理中的许多问题（例如，库存和收入管理）的特点是大动作空间和随机系统动态。这些特征使得解决问题的问题很难解决依赖于每步行动问题解决枚举技术的现有RL方法。要解决这些问题，我们开发可编程演员强化学习（PARL），一种策略迭代方法，该方法使用整数编程和示例平均近似的技术。在分析上，我们表明，对于给定的批评者，每个迭代的学习政策会聚到最佳政策，因为不确定性的底层样本转到无穷大。实际上，我们表明，即使来自潜在的不确定性的样本很少，潜在的不确定分布的正确选择的不确定分布可以在最佳的演员政策附近产生。然后，我们将算法应用于具有复杂的供应链结构的现实库存管理问题，并显示Parl优于这些设置中的最先进的RL和库存优化方法。我们发现Parl优于常用的基础股票启发式44.7％，并且在不同供应链环境中平均最高可达的RL方法高达12.1％。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can be formulated or treated as sequential decision-making problems. Some of them are offline planning problems, while others need to be solved online and are safety-critical. In this survey paper, we first describe standard RL formulations and solutions. Then we survey the landscape of existing RL-based applications in aviation. Finally, we summarize the paper, identify the technical gaps, and suggest future directions of RL research in aviation.

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests has to be performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches.

我们为处理顺序决策和外在不确定性的应用程序开发了增强学习（RL）框架，例如资源分配和库存管理。在这些应用中，不确定性仅由于未来需求等外源变量所致。一种流行的方法是使用历史数据预测外源变量，然后对预测进行计划。但是，这种间接方法需要对外源过程进行高保真模型，以确保良好的下游决策，当外源性过程复杂时，这可能是不切实际的。在这项工作中，我们提出了一种基于事后观察学习的替代方法，该方法避开了对外源过程进行建模的建模。我们的主要见解是，与Sim2real RL不同，我们可以在历史数据中重新审视过去的决定，并在这些应用程序中对其他动作产生反事实后果。我们的框架将事后最佳的行动用作政策培训信号，并在决策绩效方面具有强大的理论保证。我们使用框架开发了一种算法，以分配计算资源，以用于现实世界中的Microsoft Azure工作负载。结果表明，我们的方法比域特异性的启发式方法和SIM2REAL RL基准学习更好的政策。

组合优化是运营研究和计算机科学领域的一个公认领域。直到最近，它的方法一直集中在孤立地解决问题实例，而忽略了它们通常源于实践中的相关数据分布。但是，近年来，人们对使用机器学习，尤其是图形神经网络（GNN）的兴趣激增，作为组合任务的关键构件，直接作为求解器或通过增强确切的求解器。GNN的电感偏差有效地编码了组合和关系输入，因为它们对排列和对输入稀疏性的意识的不变性。本文介绍了对这个新兴领域的最新主要进步的概念回顾，旨在优化和机器学习研究人员。

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

蒙特卡洛树搜索（MCT）是设计游戏机器人或解决顺序决策问题的强大方法。该方法依赖于平衡探索和开发的智能树搜索。MCT以模拟的形式进行随机抽样，并存储动作的统计数据，以在每个随后的迭代中做出更有教育的选择。然而，该方法已成为组合游戏的最新技术，但是，在更复杂的游戏（例如那些具有较高的分支因素或实时系列的游戏）以及各种实用领域（例如，运输，日程安排或安全性）有效的MCT应用程序通常需要其与问题有关的修改或与其他技术集成。这种特定领域的修改和混合方法是本调查的主要重点。最后一项主要的MCT调查已于2012年发布。自发布以来出现的贡献特别感兴趣。

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.

我们考虑单个强化学习与基于事件驱动的代理商金融市场模型相互作用时学习最佳执行代理的学习动力。交易在事件时间内通过匹配引擎进行异步进行。最佳执行代理在不同级别的初始订单尺寸和不同尺寸的状态空间上进行考虑。使用校准方法考虑了对基于代理的模型和市场的影响，该方法探讨了经验性风格化事实和价格影响曲线的变化。收敛，音量轨迹和动作痕迹图用于可视化学习动力学。这表明了最佳执行代理如何在模拟的反应性市场框架内学习最佳交易决策，以及如何通过引入战略订单分类来改变模拟市场的反反应。

患者调度是一项艰巨的任务，因为它涉及处理随机因素，例如患者未知的到达流动。调度癌症患者的放射治疗治疗面临着类似的问题。治疗患者需要在推荐的最后期限内开始治疗，即入院后14或28天，而在入院后1至3天内需要迫切治疗的姑息治疗的治疗能力。大多数癌症中心通过保留用于急诊患者的固定数量的治疗槽来解决问题。然而，这种平面预留方法并不理想，并且可能在某些日子里造成急诊患者的过期治疗，同时在其他几天内没有充分利用治疗能力，这也导致治疗患者的延迟治疗。这个问题在大型和拥挤的医院中特别严重。在本文中，我们提出了一种基于预测的在线动态放射治疗调度方法。一个离线问题，其中提前已知所有未来的患者到达，以使用整数编程来解决。然后培训回归模型以识别患者到达模式之间的链接及其理想的等待时间。然后，培训的回归模型以基于预测的方法嵌入，该方法根据其特征和日历的当前状态来调度患者。数值结果表明，我们的预测方法有效地防止了应急患者的过度处理，同时与基于平面预留政策的其他调度方法相比保持良好的等待时间。

The exponential growth in demand for digital services drives massive datacenter energy consumption and negative environmental impacts. Promoting sustainable solutions to pressing energy and digital infrastructure challenges is crucial. Several hyperscale cloud providers have announced plans to power their datacenters using renewable energy. However, integrating renewables to power the datacenters is challenging because the power generation is intermittent, necessitating approaches to tackle power supply variability. Hand engineering domain-specific heuristics-based schedulers to meet specific objective functions in such complex dynamic green datacenter environments is time-consuming, expensive, and requires extensive tuning by domain experts. The green datacenters need smart systems and system software to employ multiple renewable energy sources (wind and solar) by intelligently adapting computing to renewable energy generation. We present RARE (Renewable energy Aware REsource management), a Deep Reinforcement Learning (DRL) job scheduler that automatically learns effective job scheduling policies while continually adapting to datacenters' complex dynamic environment. The resulting DRL scheduler performs better than heuristic scheduling policies with different workloads and adapts to the intermittent power supply from renewables. We demonstrate DRL scheduler system design parameters that, when tuned correctly, produce better performance. Finally, we demonstrate that the DRL scheduler can learn from and improve upon existing heuristic policies using Offline Learning.

我向已知的数学问题提出了一个深入的加强学习（RL）解决方案，称为新闻温丹主模型，这旨在考虑到概率的需求分布。为了反映更现实和复杂的情况，需求分布可以改变本周不同的日子，从而改变了最佳行为。我使用了一个双延迟的深度确定性政策梯度代理（写为完全原始代码）与演员和批评网络来解决这个问题。该代理能够学习与问题的分析解决方案一致的最佳行为，并且可以识别本周不同日期的单独概率分布并相应地行事。

强化学习（RL）是人工智能中的核心问题。这个问题包括定义可以通过与环境交互学习最佳行为的人工代理 - 其中，在代理试图最大化的奖励信号的奖励信号中定义最佳行为。奖励机（RMS）提供了一种基于Automate的基于自动机的表示，该奖励功能使RL代理能够将RL问题分解为可以通过禁止策略学习有效地学习的结构化子问题。在这里，我们表明可以从经验中学习RMS，而不是由用户指定，并且可以使用所产生的问题分解来有效地解决部分可观察的RL问题。我们将学习RMS的任务作为离散优化问题构成，其中目标是找到将问题分解为一组子问题的RM，使得其最佳记忆策略的组合是原始问题的最佳策略。我们展示了这种方法在三个部分可观察的域中的有效性，在那里它显着优于A3C，PPO和宏碁，并讨论其优点，限制和更广泛的潜力。

Deep reinforcement learning algorithms have succeeded in several challenging domains. Classic Online RL job schedulers can learn efficient scheduling strategies but often takes thousands of timesteps to explore the environment and adapt from a randomly initialized DNN policy. Existing RL schedulers overlook the importance of learning from historical data and improving upon custom heuristic policies. Offline reinforcement learning presents the prospect of policy optimization from pre-recorded datasets without online environment interaction. Following the recent success of data-driven learning, we explore two RL methods: 1) Behaviour Cloning and 2) Offline RL, which aim to learn policies from logged data without interacting with the environment. These methods address the challenges concerning the cost of data collection and safety, particularly pertinent to real-world applications of RL. Although the data-driven RL methods generate good results, we show that the performance is highly dependent on the quality of the historical datasets. Finally, we demonstrate that by effectively incorporating prior expert demonstrations to pre-train the agent, we short-circuit the random exploration phase to learn a reasonable policy with online training. We utilize Offline RL as a \textbf{launchpad} to learn effective scheduling policies from prior experience collected using Oracle or heuristic policies. Such a framework is effective for pre-training from historical datasets and well suited to continuous improvement with online data collection.

在各种现实世界应用中，组合优化问题作为混合整数线性程序（MILP）无处不在。规范的分支和结合算法通过构建越来越约束的子问题的搜索树来寻求精确解决MILP。实际上，其解决时间性能取决于启发式方法，例如选择下一个变量来约束（“分支”）。最近，机器学习（ML）已成为分支的有希望的范式。但是，先前的工作一直在努力应用强化学习（RL），理由是稀疏的奖励，艰难的探索和部分可观察性是重大挑战。取而代之的是，领先的ML方法论通过模仿学习（IL）近似高质量的手工启发式方法，这排除了新型政策的发现并需要昂贵的数据标签。在这项工作中，我们提出了复古分支。一种简单而有效的分支RL方法。通过回顾性将搜索树解构为子树中包含的多个路径，我们使代理能够从更短的轨迹中学习具有更可预测的下一步状态。在对四个组合任务的实验中，我们的方法可以在没有任何专家指导或预培训的情况下学习分支。我们的表现优于当前最新的RL分支算法，比最佳IL方法在MILPS上具有500个约束和1000个变量的最佳性能的20％以内，并验证了我们的回顾性构建轨迹对于实现的必要这些结果。

Adequately assigning credit to actions for future outcomes based on their contributions is a long-standing open challenge in Reinforcement Learning. The assumptions of the most commonly used credit assignment method are disadvantageous in tasks where the effects of decisions are not immediately evident. Furthermore, this method can only evaluate actions that have been selected by the agent, making it highly inefficient. Still, no alternative methods have been widely adopted in the field. Hindsight Credit Assignment is a promising, but still unexplored candidate, which aims to solve the problems of both long-term and counterfactual credit assignment. In this thesis, we empirically investigate Hindsight Credit Assignment to identify its main benefits, and key points to improve. Then, we apply it to factored state representations, and in particular to state representations based on the causal structure of the environment. In this setting, we propose a variant of Hindsight Credit Assignment that effectively exploits a given causal structure. We show that our modification greatly decreases the workload of Hindsight Credit Assignment, making it more efficient and enabling it to outperform the baseline credit assignment method on various tasks. This opens the way to other methods based on given or learned causal structures.

Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a period of time in the future. Motivated by this observation, we make an attempt to optimize the distribution of demand to handle this problem by learning the long-term spatio-temporal values as a guideline for pricing strategy. In this study, we propose an offline deep reinforcement learning based method focusing on the demand side to improve the utilization of transportation resources and customer satisfaction. We adopt a spatio-temporal learning method to learn the value of different time and location, then incentivize the ride requests of passengers to adjust the distribution of demand to balance the supply and demand in the system. In particular, we model the problem as a Markov Decision Process (MDP).