Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning
Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too l…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningSpatio-Temporal ForecastingMulti-Dimensional Optimization for Text Summarization via Reinforcement Learning
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a specific dimension, facing challenges in gen…
Multi-Objective Reinforcement LearningMulti-Task Learningreinforcement-learningReinforcement Learning+1Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prio…
Autonomous DrivingDecision MakingMulti-Objective Reinforcement Learningreinforcement-learningSafe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints. To tackle this issu…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
Multi-task reinforcement learning (MTRL) has shown great promise in many real-world applications. Existing MTRL algorithms often aim to learn a policy that optimizes individual objective functions simultaneously with a g…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningGeneralized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks
We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6G…
Autonomous DrivingAutonomous VehiclesMulti-Objective Reinforcement LearningFinite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcem…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningUCB-driven Utility Function Search for Multi-objective Reinforcement Learning
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MORL based on decomposition is a family of …
Decision MakingMuJoCoMulti-Objective Reinforcement Learningreinforcement-learning+1Demonstration Guided Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to diverse user preferences, traditional re…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningConflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
In many real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines. To address these considerations, we propose a constrained multi-objective RL algor…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue (SAR) missions presents a promising avenue for enhancing operational efficiency and effectiveness. However, the success of these missions is not s…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningTrajectory PlanningDynamic Multi-Reward Weighting for Multi-Style Controllable Generation
Textual style expresses a diverse set of information, including interpersonal dynamics (e.g., formality) and the author's emotions or attitudes (e.g., disgust). An open question is how language models can be explicitly c…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization
RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions. However, in many target applications, the natural reward functions…
Language ModellingMachine TranslationMulti-Objective Reinforcement LearningStyle TransferSurpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning
Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns involves multiple objectives and constraints, leading to a vast number of candidate solutions that cannot be explicitly sol…
Deep Reinforcement LearningMulti-Objective Reinforcement LearningStochastic OptimizationPMGDA: A Preference-based Multiple Gradient Descent Algorithm
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given …
Multi-Objective Reinforcement LearningMulti-Task Learningreinforcement-learningReinforcement LearningDivide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
An important challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies to attain optimal performance under different preferences. We introduce Iterated Pareto Referent Optimisation (IPRO…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningValue function interference and greedy action selection in value-based multi-objective reinforcement learning
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. W…
Multi-Objective Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those re…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning
Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while …
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningInferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a promising approach to mitigate this proble…
Decision Makingenergy managementManagementMulti-Objective Reinforcement Learning