Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[HTML][HTML] Deep reinforcement learning for process design: Review and perspective
The transformation toward renewable energy and feedstock supply in the chemical industry
requires new conceptual process design approaches. Recently, deep reinforcement …
requires new conceptual process design approaches. Recently, deep reinforcement …
Decision-making under uncertainty: beyond probabilities: Challenges and perspectives
This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …
Foundations of multivariate distributional reinforcement learning
In reinforcement learning (RL), the consideration of multivariate reward signals has led to
fundamental advancements in multi-objective decision-making, transfer learning, and …
fundamental advancements in multi-objective decision-making, transfer learning, and …
Uncertainty-aware constraint inference in inverse constrained reinforcement learning
Aiming for safe control, Inverse Constrained Reinforcement Learning (ICRL) considers
inferring the constraints respected by expert agents from their demonstrations and learning …
inferring the constraints respected by expert agents from their demonstrations and learning …
Cem: Constrained entropy maximization for task-agnostic safe exploration
In the absence of assigned tasks, a learning agent typically seeks to explore its environment
efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored …
efficiently. However, the pursuit of exploration will bring more safety risks. An under-explored …
Trust region-based safe distributional reinforcement learning for multiple constraints
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints
must be met, such as avoiding collisions, limiting energy consumption, and maintaining …
must be met, such as avoiding collisions, limiting energy consumption, and maintaining …
[HTML][HTML] Constrained continuous-action reinforcement learning for supply chain inventory management
Reinforcement learning (RL) is a promising solution for difficult decision-making problems,
such as inventory management in chemical supply chains. However, enabling RL to …
such as inventory management in chemical supply chains. However, enabling RL to …
Feasible reachable policy iteration
The goal-reaching tasks with safety constraints are common control problems in real world,
such as intelligent driving and robot manipulation. The difficulty of this kind of problem …
such as intelligent driving and robot manipulation. The difficulty of this kind of problem …
Safe reinforcement learning on the constraint manifold: Theory and applications
Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …
promising for solving complex problems in unstructured environments. However, most …
Resilient constrained learning
When deploying machine learning solutions, they must satisfy multiple requirements beyond
accuracy, such as fairness, robustness, or safety. These requirements are imposed during …
accuracy, such as fairness, robustness, or safety. These requirements are imposed during …