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
A survey on explainable reinforcement learning: Concepts, algorithms, challenges
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …
Attention-guided contrastive role representations for multi-agent reinforcement learning
Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …
Long-short-view aware multi-agent reinforcement learning for signal snippet distillation in delirium movement detection
Automatic movement analysis utilizing surveillance video is believed to be an important and
convenient way for timely delirium detection in an Intensive Care Unit (ICU). However, video …
convenient way for timely delirium detection in an Intensive Care Unit (ICU). However, video …
Interaction pattern disentangling for multi-agent reinforcement learning
Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable
success over a wide spectrum of complex control tasks. However, recent advances in multi …
success over a wide spectrum of complex control tasks. However, recent advances in multi …
Egocentric 3D Skeleton Learning in a Deep Neural Network Encodes Obese-like Motion Representations
Obesity is a growing health concern, mainly caused by poor dietary habits. Yet, accurately
tracking the diet and food intake of individuals with obesity is challenging. Although 3D …
tracking the diet and food intake of individuals with obesity is challenging. Although 3D …
ISFORS-MIX: Multi-agent reinforcement learning with Importance-Sampling-Free Off-policy learning and Regularized-Softmax Mixing network
J Rao, C Wang, M Liu, J Lei, W Giernacki - Knowledge-Based Systems, 2025 - Elsevier
In multi-agent reinforcement learning (MARL), the low quality of value function and the
estimation bias and variance in value function decomposition (VFD) are critical challenges …
estimation bias and variance in value function decomposition (VFD) are critical challenges …
Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize
the voltage levels to ensure efficient and reliable operation of power systems. With the …
the voltage levels to ensure efficient and reliable operation of power systems. With the …
CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representation
Context-based offline meta-reinforcement learning (OMRL) methods have achieved
appealing success by leveraging pre-collected offline datasets to develop task …
appealing success by leveraging pre-collected offline datasets to develop task …
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning
The significant role of division of labor (DOL) in promoting cooperation is widely recognized
in real-world applications. Many cooperative multi-agent reinforcement learning (MARL) …
in real-world applications. Many cooperative multi-agent reinforcement learning (MARL) …
Heterogeneous Value Decomposition Policy Fusion for Multi-Agent Cooperation
Value decomposition (VD) has become one of the most prominent solutions in cooperative
multi-agent reinforcement learning. Most existing methods generally explore how to factorize …
multi-agent reinforcement learning. Most existing methods generally explore how to factorize …