Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
Radiology Report Generation via Multi-objective Preference Optimization
Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches rely on supervised regression based on different architectures or additi…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more complex than single-objective RL, partic…
FairnessMulti-Objective Reinforcement LearningNavigating Trade-offs: Policy Summarization for Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is used to solve problems involving multiple objectives. An MORL agent must make decisions based on the diverse signals provided by distinct reward functions. Training an MOR…
ClusteringMulti-Objective Reinforcement LearningAdaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs and values. We contribute to this pursu…
Multi-Objective Reinforcement LearningMulti-objective Reinforcement Learning: A Tool for Pluralistic Alignment
Reinforcement learning (RL) is a valuable tool for the creation of AI systems. However it may be problematic to adequately align RL based on scalar rewards if there are multiple conflicting values or stakeholders to be c…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previous dominating MORL methods typically gene…
continuous-controlContinuous ControlMulti-Objective Reinforcement LearningInferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
Many decision-making problems feature multiple objectives where it is not always possible to know the preferences of a human or agent decision-maker for different objectives. However, demonstrated behaviors from the deci…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement LearningStage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach
As the complexity of tasks addressed through reinforcement learning (RL) increases, the definition of reward functions also has become highly complicated. We introduce an RL method aimed at simplifying the reward-shaping…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences for each objective. In most existing stu…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning
Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and multiple requirements presents considerable challenges. In this paper, we propo…
Long-Context UnderstandingMulti-Objective Reinforcement LearningReinforcement Learning (RL)Zero-Shot LearningMODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
Multi-objective Reinforcement Learning (MORL) seeks to develop policies that simultaneously optimize multiple conflicting objectives, but it requires extensive online interactions. Offline MORL provides a promising solut…
Multi-Objective Reinforcement LearningMulti-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems
Reinforcement Learning (RL) is used extensively in Autonomous Systems (AS) as it enables learning at runtime without the need for a model of the environment or predefined actions. However, most applications of RL in AS, …
Deep Reinforcement LearningMulti-Objective Reinforcement LearningQ-Learningreinforcement-learning+2Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
This paper investigates multi-objective reinforcement learning (MORL), which focuses on learning Pareto optimal policies in the presence of multiple reward functions. Despite MORL's significant empirical success, there i…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningIn Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functions. However, MORL is challenging due to …
Multi-Objective Reinforcement LearningQ-LearningA Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences in appliance scheduling, traditional MO…
energy managementMeta-LearningMulti-Objective Reinforcement LearningSchedulingLearning Pareto Set for Multi-Objective Continuous Robot Control
For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and…
Multi-Objective Reinforcement LearningOnline Pareto-Optimal Decision-Making for Complex Tasks using Active Inference
When a robot autonomously performs a complex task, it frequently must balance competing objectives while maintaining safety. This becomes more difficult in uncertain environments with stochastic outcomes. Enhancing trans…
Decision MakingMulti-Objective Reinforcement LearningThe Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min framework focusing on fairness among th…
FairnessMulti-Objective Reinforcement Learningreinforcement-learningReinforcement LearningMulti-objective Reinforcement learning from AI Feedback
This paper presents Multi-Objective Reinforcement Learning from AI Feedback (MORLAIF), a novel approach to improving the alignment and performance of language models trained using reinforcement learning from AI feedback …
Language ModelingLanguage ModellingMulti-Objective Reinforcement Learningreinforcement-learning+1Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural maintenance optimization, an area traditionall…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)