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In the domain of complex urban traffic networks, real-time Traffic Signal Control (TSC) serves as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple intersections involves considerable complexity. Deep Reinforcement Learning (DRL) has emerged as a robust solution. In recent years, there has been rapid advancement in TSC methods, with numerous researchers employing various novel DRL methodologies. Yet, existing surveys lack timeliness and universality in capturing the latest research. There is a notable gap in current research surveys with respect to the latest developments in TSC. Therefore, the focus of this paper lies in analyzing the most recent papers from the past five years, with the aim to provide a comprehensive and multi-dimensional review of the evolution of DRL in TSC. The survey categorizes current research based on model setups, utilized algorithms, and application scenarios. Finally, this paper highlights potential directions for future TSC research. [ABSTRACT FROM AUTHOR]

Maintenance planning of networked multi-asset systems is a complex problem due to the inherent individual and collective asset constraints and dynamics as well as the size of the system and interdependencies among assets. Although multi-asset systems have been studied numerous times in the past decades, maintenance planning implications of the system's network characteristics have been barely analysed. Likewise, solutions that consider the network perspective suffer from scalability issues as a network-wide observability is assumed. This paper proposes a network maintenance planning approach based on the decomposition of the multi-asset network into fixed-size localised subnetworks. The overall network maintenance plan is produced by aggregating the subnetwork maintenance plans, which are computed independently via a multi-agent deep reinforcement learning (MARL) algorithm. The results are evaluated against a network-wide approach as well as the commonly-used individual approach. The paper also introduces a systematic approach to integrate the MARL resulting policy in a multi-asset agent-based model. Simulation results of several random asset networks and a large nationwide network infrastructure show that, although a network-wide approach outperforms, on average, other approaches considered, the localised subnetworks approach, provides an acceptable alternative in networks with small-world properties, without the need of a network-wide view. • A Network-wide maintenance approach outperforms individual approaches in networked assets with the topologies analysed. • In analysed networks with small-world properties, the Localised approach shows performance close to the network-wide approach. • A systematic integration of MARL and multi-asset agent-based modelling enables evaluation of maintenance approaches. [ABSTRACT FROM AUTHOR]

Due to the increasing high penetration of Photovoltaic (PV), it brings great challenge for voltage control issue of distribution network. To address this problem, this paper presents an improved collaborative Multi-Agent Reinforcement Learning (MARL) approach for proactive voltage control, aimed at mitigating voltage violations and minimizing power losses. The self-attention mechanism is embedded into the multi-agent soft actor-critic (MASAC) algorithm to enhance the collaboration of multi-agent system, which can well improve the learning efficiency to ensure the voltage safety of Distribution Network. In addition, the proposed learning approach is implemented on IEEE 33-bus, 141-bus and 322-bus systems, and the simulation results reveal that the proposed approach can control the voltage into safety domain as well as reduce power losses. [ABSTRACT FROM AUTHOR]

Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) – the global–local trade-off. Deep learning models inspired by different data generation processes aim to combine the benefits of global and local approaches. Specifically, these frequently propose feeding the statistical expressiveness of classical local models into more complex global networks. Following recent trends in neural networks, the theoretical foundations of these hybrid models can also explain the surge of Transformer-based time series forecasting applications, which showcase potential benefits for the global and local equilibrium. Dynamic reinforcement learning (RL) models have also been explored to optimize the balance of global and local signals in general prediction problems, frequently through Q-learning algorithms. RL models can be proposed to dynamically adjust the influence of global versus local information to improve predictive performance at both scales. This paper conducted a concise literature review focused on these two research streams to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyzes (PRISMA) guidelines and achieved a selection of 143 publications since 2000. The main findings reveal that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered. This review advances knowledge at the intersection of two distinct yet potentially complementary research areas, identifying opportunities to combine different methodological approaches in addressing the global–local trade-off in forecasting collections of time series. This is achieved by surfacing shared limitations in current research and presenting avenues for integrating distinct methodologies, namely by further developing the theoretical underpinnings of reinforcement learning techniques. With this work we seek to enhance understanding of the relevant research landscape and help inform future solutions by establishing a foundation for collaborative work. [Display omitted] • Review of global and local time series forecasting approaches including Deep Learning (DL) models combining global and local signals and Dynamic Reinforcement Learning (RL) methods optimizing this trade-off • Identification of opportunities to combine distinct forecasting methodologies like global DL models and RL based local modeling to address the global–local trade-off. • Search for ways to integrate RL techniques for time series forecasting by further developing their theoretical underpinnings based on limitations found in current research. • Main findings show global models achieve strong expressiveness capturing complex patterns while enabling probabilistic outcomes, and RL methods mitigate risks when predicting each step for each series [ABSTRACT FROM AUTHOR]

Operational forecasting often requires predicting collections of related, multivariate time series data that are high-dimensional in nature. This can be tackled by fitting a single function to all series (global approach) or assuming each series as a separate prediction problem and fitting one function to each (local approach) – the global–local trade-off. Deep learning models inspired by different data generation processes aim to combine the benefits of global and local approaches. Specifically, these frequently propose feeding the statistical expressiveness of classical local models into more complex global networks. Following recent trends in neural networks, the theoretical foundations of these hybrid models can also explain the surge of Transformer-based time series forecasting applications, which showcase potential benefits for the global and local equilibrium. Dynamic reinforcement learning (RL) models have also been explored to optimize the balance of global and local signals in general prediction problems, frequently through Q-learning algorithms. RL models can be proposed to dynamically adjust the influence of global versus local information to improve predictive performance at both scales. This paper conducted a concise literature review focused on these two research streams to optimize the balance between globality and locality in forecasting collections of time series. It focuses on their evolution across time and hints at opportunities to close some of the research gaps by intersecting both propositions. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyzes (PRISMA) guidelines and achieved a selection of 143 publications since 2000. The main findings reveal that global models have achieved strong expressiveness in capturing the most complex structural patterns while still enabling probabilistic outcomes to be delivered through uncertainty estimates. On the other hand, RL based methods depict great benefits in mitigating the risks of generalization by imprinting contextual diversity when predicting each step ahead for each series. Within those, the adoption of other computational learning or evolutionary-based methods (e.g. Genetic Programming) to improve the parametrization of the learning policies is also highlighted as an area of future work yet to be uncovered. This review advances knowledge at the intersection of two distinct yet potentially complementary research areas, identifying opportunities to combine different methodological approaches in addressing the global–local trade-off in forecasting collections of time series. This is achieved by surfacing shared limitations in current research and presenting avenues for integrating distinct methodologies, namely by further developing the theoretical underpinnings of reinforcement learning techniques. With this work we seek to enhance understanding of the relevant research landscape and help inform future solutions by establishing a foundation for collaborative work. [Display omitted] • Review of global and local time series forecasting approaches including Deep Learning (DL) models combining global and local signals and Dynamic Reinforcement Learning (RL) methods optimizing this trade-off • Identification of opportunities to combine distinct forecasting methodologies like global DL models and RL based local modeling to address the global–local trade-off. • Search for ways to integrate RL techniques for time series forecasting by further developing their theoretical underpinnings based on limitations found in current research. • Main findings show global models achieve strong expressiveness capturing complex patterns while enabling probabilistic outcomes, and RL methods mitigate risks when predicting each step for each series [ABSTRACT FROM AUTHOR]

This paper addresses a crucial issue in the airline industry by tackling a dynamic pricing problem in the presence of patient customers, a scenario that has gained significance due to the revenue loss of airlines caused by customers' non-myopic decision-making. To effectively capture this non-myopic characteristic, we propose a Markov decision process (MDP) including a history of offered prices as a state variable. In contrast to previous studies, distributions of customers' properties are assumed to be unknown in advance for a more realistic representation of real-world scenarios. To deal with the new challenges of the problem, we propose utilizing a specific learning framework (i.e., deep exploration-based RL) that is unexplored in this domain. The numerical experiments demonstrate that its performance can be improved on the MDP we designed and show that it outperforms the benchmark algorithm. The structures of pricing policies generated from the bootstrapped deep Q-network algorithm imply that airlines should offer high and low prices alternately from the beginning of the sales period rather than increasing prices as time goes on. We also ascertain that more frequent consecutive high-priced periods can increase airlines' revenue in environments with higher customer patience levels. [ABSTRACT FROM AUTHOR]

In recent years, consortium blockchains have attracted considerable interest from the business communities and academia. To satisfy specific application requirements, appropriately configuring or adjusting the parameters is essential but challenging for developers when building the consortium blockchain. In this paper, the parameter configuration and adjustment for the consortium blockchain is transformed as a multi-objective optimization problem, and a novel explainable deep reinforcement learning (DRL) algorithm is proposed to solve the problem. On considering that existing DRL algorithms cannot be directly used in the consortium blockchain as they suffer from lacking of explainability, a causal model for configuring and adjusting the consortium blockchain's parameters is proposed and integrated into the DRL algorithm. The causal model can be used to derive causal explanations of the DRL algorithm to increase its trustworthiness. Furthermore, the causal model-based DRL (C-DRL) algorithm can perform causal inference before taking action to eliminate unreasonable exploration and improve the DRL algorithm's performance. The experimental results demonstrate the proposed algorithm provides the consortium blockchain with adaptive parameter configuration and adjustment to achieve sustainable high performance and security. Furthermore, the proposed algorithm increases the convergence speed of the vanilla algorithm by 49.3% and is more trustworthy than the vanilla DRL algorithm. • To achieve sustainable high performance and security, we transform the parameter configuration and adjustment for the consortium blockchain as a multi-objective optimization problem and provide a formal representation of the problem. • We propose a causal model of consortium blockchain's parameters using the structural causal model (SCM) formalism. • We propose a causal model-based DRL (C-DRL) algorithm to solve the multi-objective optimization problem. The C-DRL algorithm is equipped with the causal model of consortium blockchain's parameters. • The agent can use the causal model to derive the causal explanation for its action to provide trustworthiness. Furthermore, the agent with the causal model can utilize causal inference to eliminate unreasonable exploration and improve the convergence speed of the current DRL algorithm. • Extensive experiments are conducted to validate our proposal. The simulation results show that the agent running the C-DRL algorithm has more reasonable behavior than that running the model-free DRL algorithm. Moreover, the C-DRL algorithm converges faster than the model-free DRL algorithm. [ABSTRACT FROM AUTHOR]

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users' healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel "Pattern-Proof Malware Validation" (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%. [ABSTRACT FROM AUTHOR]

Astronomical seeing is the primary indicator of the observation quality of large-scale optical telescopes. Forecasting astronomical seeing is an essential and challenging task in the operation of large optical telescopes, as it can help observers more efficiently arrange observation tasks as well as providing hints for mechanism optimization of optical telescopes. In this paper, we propose an efficient data-driven astronomical seeing forecasting method by leveraging the environment monitoring data from multiple sensors deployed with optical telescopes. The basic idea is to use a large number of random yet fixed convolution kernels to extract features from the original data, and then train a simple tree-based predictor based on these features. In particular, the raw monitoring data is transformed by differential operations, enabling a more explicit representation of spatial and temporal features; then an elaborate convolution mechanism, including the convolution kernel and the pooling operator, is designed to extract a variety set of features of the input time series, which serve as the foundation for constructing the time series forecasting model. In contrast to existing deep learning models, there is no need to calculate and back-propagate the gradients during the training procedure, making the proposed method much more efficient. We collect real-world monitoring data from Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), one of the world's largest optical reflecting telescopes, and conduct extensive empirical evaluations. The experimental results demonstrate that the proposed method can achieve state-of-the-art prediction accuracy, i.e., 0.1125 in MAE, 0.0252 in MSE, and 0.0322 in MAPE, outperforming most deep-learning-based forecasting models. Moreover, it surpasses both machine learning and deep learning approaches in terms of training efficiency with a significant margin. [Display omitted] • The task of forecasting astronomical seeing for optical telescopes is investigated. • A variety of real-world monitoring data of LAMOST is collected. • A new multivariate time series forecasting method — SeeROCKET is proposed. • SeeROCKET achieves very promising performance both in accuracy and efficiency. [ABSTRACT FROM AUTHOR]