Learning from policy experimentation is a promising way to approach the “wicked problem” of climate adaptation, which is characterised by knowledge gaps and contested understandings of future risk. However, although the role of learning in shaping pu...
In reinforcement learning, off-policy temporal difference learning methods have gained significant attention due to their flexibility in utilizing existing data. However, traditional off-policy temporal difference methods often suffer from poor conve...
Local governments play a critical role in advancing climate change mitigation under national carbon neutrality strategies; however, practical mechanisms for policy learning and collaboration remain limited. This study analyzes the Kyoto Roundtable, a...
Social learning is the main policy-design mechanism that involves interactions between agents. This study provides an overview of the research on policy design using social learning. Descriptive and co-citation analyses were used to identify emerging...
Autonomous learning of robotic skills seems to be more natural and more practical than engineered skills, analogous to the learning process of human individuals. Policy gradient methods are a type of reinforcement learning technique which have great...
Temporal difference (TD) learning is a powerful framework for value function approximation in reinforcement learning. However, standard TD methods often struggle with feature representation and off-policy learning challenges. In this paper, we propos...
Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem...
The implementation of Indonesia’s Merdeka Belajar policy requires transformative leadership approaches to address post-COVID-19 educational disruption. This study examines the associations between e-leadership, the adoption of disruptive learni...
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk a...
Compared to traditional data-driven learning methods, recently developed deep reinforcement learning (DRL) approaches can be employed to train robot agents to obtain control policies with appealing performance. However, learning control policies for...
Adversarial policies provide a practical means of stress-testing reinforcement-learning agents by inducing failure through environment interactions without modifying the deployed victim policy. Learning effective adversarial policies under partial ob...
Poker has been considered a challenging problem in both artificial intelligence and game theory because poker is characterized by imperfect information and uncertainty, which are similar to many realistic problems like auctioning, pricing, cyber secu...
Based on the good performance of deep reinforcement learning (DRL) in policy optimization, a stereoscopic projection policy optimization method is proposed, which combines the simulation experiment method with the DRL method. On the basis of policy o...
Invalid action masking is a practical technique in deep reinforcement learning to prevent agents from taking invalid actions. Existing approaches rely on action masking during policy training and utilization. This study focuses on developing reinforc...
Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment. It is an important approach to solving sequential decision...
This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architec...
To address the problem of an unknown performance index in discrete-time (DT) linear systems with unobservable states, this paper investigates the output-feedback inverse reinforcement learning (IRL) problem and proposes a data-driven output-feedback...
Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and i...
In this paper, we review the EU and national (Croatian) education policy trends related to life-long learning and adult education in the last five years, focusing on the subsequent crises (COVID-19 and the security crisis caused by the war in Ukraine...
Games provide an excellent testing ground for machine learning and artificial intelligence, offering diverse environments with strategic challenges and complex decision-making scenarios. This study seeks to design a self-learning artificial intellige...
The global maritime industry faces a conflict between economic competition and sustainability standards. Economic pressure often incentivizes ship registries toward regulatory leniency, degrading environmental and social standards. Traditional static...
Precision irrigation and optimization of water use have become essential factors in agriculture because water is critical for crop growth. The proper management of an irrigation system should enable the farmer to use water efficiently to increase pro...
Student attrition at tertiary institutions is a global challenge with significant personal and social consequences. Early identification of students at risk of dropout is crucial for proactive and preventive intervention. This study presents a machin...
The imperative for swift and intelligent decision making in production scheduling has intensified in recent years. Deep reinforcement learning, akin to human cognitive processes, has heralded advancements in complex decision making and has found appl...
In terms of deep reinforcement learning (RL), exploration is highly significant in achieving better generalization. In benchmark studies, ε-greedy random actions have been used to encourage exploration and prevent over-fitting, thereby improv...
Feed-in tariff (FiT) policy approaches for renewable energy (RE) deployment are employed in many nations around the world. Although FiTs are considered effective in boosting RE deployment, the issue of increasing energy bills and social burden is an...
This paper introduces a policy-search-based reinforcement learning algorithm aimed at generating optimal set-points of wind turbines in wind farms. The proposed approach addresses the problem of multivariable optimization in systems where the objecti...
Direct policy learning (DPL) is a widely used approach in imitation learning for time-efficient and effective convergence when training mobile robots. However, using DPL in real-world applications is not sufficiently explored due to the inherent chal...
The economic crises of recent years, the COVID-19 pandemic and growing dissatisfaction with the quality of education have proved that national education systems throughout the world require large-scale transformations. Effective implementation of the...
Low-altitude micro downbursts pose a severe threat to aviation safety, yet conventional control approaches and standard deep reinforcement learning (DRL) often fail due to explicit modeling difficulties and sparse reward constraints. To address these...
The development of the new energy vehicle (NEV) industry has become a key driver of the global low-carbon transition. Understanding the policy effect on NEV diffusion is essential to promote sustainable growth. In this study, we propose a new approac...
The Chinese government releases a large number of enterprise-friendly policy documents every year, but due to the dispersed policy release channels and complex document parsing, enterprises need to invest a lot of human and material resources to anal...
This paper explores the initiation process of the long-term care insurance (LTCI) policy in China’s social security sector, and focuses on policy-oriented learning activities to develop initiatives from German lessons. It presents a case from the LTC...
With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and ac...
Offline reinforcement learning aims to learn effective policies from fixed datasets without further interaction with the environment. However, limited behavior-policy coverage can lead to value-estimation errors and unreliable policy improvement, par...
In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method ta...
Translating large-scale motion datasets into robust, deployable humanoid controllers is a critical challenge in engineering informatics, primarily due to the scarcity of high-quality annotations, the risk of mode collapse in conditional generation, a...
This article investigates the optimal tracking control problem for data-based stochastic discrete-time linear systems. An average off-policy Q-learning algorithm is proposed to solve the optimal control problem with random disturbances. Compared with...
In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the...
Finite-control-set model predictive control provides excellent torque–speed regulation for switched reluctance motor drives but requires an online combinatorial search at every control instant, making low-cost embedded implementation challengin...
This study presents a novel approach to a mixed-integer linear programming (MILP) model for periodic inventory management that combines reinforcement learning algorithms. The rolling horizon method (RHM) is a multi-period optimization approach that i...
As industrial green transformation advances and urban risks become more complex, this study explores the relationship between China’s national green industrial park policy and urban resilience. The empirical analysis draws on panel data for 293...
Integrated water resources management (IWRM) has become a global paradigm for the governance of surface, coastal and groundwater. International bodies such as the European Union, the Global Water Partnership, and the United Nations have taken the lea...
The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient p...
This paper presents a novel approach to economic dispatch (ED) optimization in power systems through the application of Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm. The economic dispatch problem, a fundamental cha...
This paper investigates a parameter-free H∞ differential game approach for nonlinear active vehicle suspensions. The study accounts for the geometric nonlinearity of the half-car active suspension and the cubic nonlinearity of the damping eleme...
Once an active distribution network of a cyber-physical system is in alert state, it is vulnerable to cross-domain cascading failures. It is necessary to transit the state of an active distribution network of cyber-physical system from an alert state...
The coupling of deep reinforcement learning to numerical flow control problems has recently received considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of...
A machine learning-based policy–utility framework was developed to assess trap mesh sizes (35–80 mm) in the Marbled Flounder fishery and reframe traditional selectivity analysis into a policy-oriented decision context. A utility function...
The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible...
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