Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-object…
Deep Reinforcement LearningManagementMulti-Objective Reinforcement Learningreinforcement-learning+1Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (…
ManagementMulti-Objective Reinforcement LearningFairDICE: Fairness-Driven Offline Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) aims to optimize policies in the presence of conflicting objectives, where linear scalarization is commonly used to reduce vector-valued returns into scalar signals. While ef…
FairnessMulti-Objective Reinforcement Learningreinforcement-learningReinforcement LearningBenchmarking MOEAs for solving continuous multi-objective RL problems
Multi-objective reinforcement learning (MORL) addresses the challenge of simultaneously optimizing multiple, often conflicting, rewards, moving beyond the single-reward focus of conventional reinforcement learning (RL). …
BenchmarkingEvolutionary AlgorithmsMulti-Objective Reinforcement Learningreinforcement-learning+2Developing and Integrating Trust Modeling into Multi-Objective Reinforcement Learning for Intelligent Agricultural Management
Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools such as remote sensing, intelligent irrigation, fertilization management, and crop simulation to improve agricultural efficiency and…
ManagementMulti-Objective Reinforcement LearningReinforcement Learning (RL)Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
Industrial automation increasingly demands energy-efficient control strategies to balance performance with environmental and cost constraints. In this work, we present a multi-objective reinforcement learning (MORL) fram…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningMulti-Objective Reinforcement Learning for Adaptive Personalized Autonomous Driving
Human drivers exhibit individual preferences regarding driving style. Adapting autonomous vehicles to these preferences is essential for user trust and satisfaction. However, existing end-to-end driving approaches often …
Autonomous DrivingAutonomous VehiclesCollision AvoidanceMulti-Objective Reinforcement Learning+2Active Sampling for MRI-based Sequential Decision Making
Despite the superior diagnostic capability of Magnetic Resonance Imaging (MRI), its use as a Point-of-Care (PoC) device remains limited by high cost and complexity. To enable such a future by reducing the magnetic field …
Decision MakingDiagnosticMulti-Objective Reinforcement LearningSequential Decision Making+1EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuning
Recent advances in reinforcement learning (RL) for large language model (LLM) fine-tuning show promise in addressing multi-objective tasks but still face significant challenges, including complex objective balancing, low…
Ensemble LearningLarge Language ModelMulti-Objective Reinforcement LearningReinforcement Learning (RL)A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game
In practical application, the pursuit-evasion game (PEG) often involves multiple complex and conflicting objectives. The single-objective reinforcement learning (RL) usually focuses on a single optimization objective, an…
Multi-Objective Reinforcement LearningQ-LearningReinforcement Learning (RL)Closing the Intent-to-Behavior Gap via Fulfillment Priority Logic
Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring…
continuous-controlContinuous ControlMulti-Objective Reinforcement Learningreinforcement-learning+1On Generalization Across Environments In Multi-Objective Reinforcement Learning
Real-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an increasingly prominent field of research. De…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Multi-Objective Reinforcement Learning for Critical Scenario Generation of Autonomous Vehicles
Autonomous vehicles (AVs) make driving decisions without human intervention. Therefore, ensuring AVs' dependability is critical. Despite significant research and development in AV development, their dependability assuran…
Autonomous VehiclesMulti-Objective Reinforcement LearningQ-LearningBone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation
User information needs are often highly diverse and varied. A key challenge in current research is how to achieve controllable multi-objective generation while enabling rapid adaptation to accommodate diverse user demand…
Multi-Objective Reinforcement LearningAerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication ra…
Deep Reinforcement LearningMulti-Objective Reinforcement LearningMol-MoE: Training Preference-Guided Routers for Molecule Generation
Recent advances in language models have enabled framing molecule generation as sequence modeling. However, existing approaches often rely on single-objective reinforcement learning, limiting their applicability to real-w…
BenchmarkingDrug DesignMixture-of-ExpertsMulti-Objective Reinforcement Learning+2Multi-Objective Reinforcement Learning for Power Grid Topology Control
Transmission grid congestion increases as the electrification of various sectors requires transmitting more power. Topology control, through substation reconfiguration, can reduce congestion but its potential remains und…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningTowards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. …
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Pareto Set Learning for Multi-Objective Reinforcement Learning
Multi-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcement Learning (RL) in optimizing decision…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning, multi-objective reinforcement learning,…
Multi-Objective Reinforcement LearningMulti-Task Learning