Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function. However issues can arise with this…
Multi-Objective Reinforcement LearningQ-LearningPolicy-regularized Offline Multi-objective Reinforcement Learning
In this paper, we aim to utilize only offline trajectory data to train a policy for multi-objective RL. We extend the offline policy-regularized method, a widely-adopted approach for single-objective offline RL problems,…
Multi-Objective Reinforcement LearningOffline RLreinforcement-learningReinforcement LearningHuman-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) aims to find a set of high-performing and diverse policies that address trade-offs between multiple conflicting objectives. However, in practice, decision makers (DMs) often …
Decision MakingManagementMulti-Objective Reinforcement LearningMulti-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evalua…
Multi-Objective Reinforcement Learningreinforcement-learningPromptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences
Customizing robotic behaviors to be aligned with diverse human preferences is an underexplored challenge in the field of embodied AI. In this paper, we present Promptable Behaviors, a novel framework that facilitates eff…
Multi-Objective Reinforcement LearningRobot NavigationEnhancing Robotic Navigation: An Evaluation of Single and Multi-Objective Reinforcement Learning Strategies
This study presents a comparative analysis between single-objective and multi-objective reinforcement learning methods for training a robot to navigate effectively to an end goal while efficiently avoiding obstacles. Tra…
Multi-Objective Reinforcement LearningNavigatereinforcement-learningReinforcement LearningAdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design
A central challenge of the clean energy transition is the development of catalysts for low-emissions technologies. Recent advances in Machine Learning for quantum chemistry drastically accelerate the computation of catal…
Deep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learningMulti-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework
Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solv…
Multi-Objective Reinforcement Learningreinforcement-learningMulti-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return
We study multi-objective reinforcement learning with nonlinear preferences over trajectories. That is, we maximize the expected value of a nonlinear function over accumulated rewards (expected scalarized return or ESR) i…
FairnessMulti-Objective Reinforcement Learningreinforcement-learningHyperparameter Optimization for Multi-Objective Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agen…
Hyperparameter OptimizationMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Controlled Decoding from Language Models
KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective and propose a modular solver for it, call…
Language ModelingLanguage ModellingMulti-Objective Reinforcement Learningreinforcement-learning+2On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning
Most algorithms in reinforcement learning (RL) require that the objective is formalised with a Markovian reward function. However, it is well-known that certain tasks cannot be expressed by means of an objective in the M…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning diverse, individual perspectives. In this work, we stud…
Language ModelingLanguage ModellingLarge Language ModelMulti-Objective Reinforcement Learning+2Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the challenges of navigating dynamic environments with obstacles like buildings and wind. This paper presents a method that combines multi-objective rei…
Drone navigationMulti-Objective Reinforcement Learningreinforcement-learningGMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing
Computerized Adaptive Testing(CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical response records. Most CAT methods only focus o…
DiversityGraph Neural NetworkMulti-Objective Reinforcement LearningA Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct…
BenchmarkingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Distributional Pareto-Optimal Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) has been proposed to learn control policies over multiple competing objectives with each possible preference over returns. However, current MORL algorithms fail to account fo…
Autonomous DrivingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement LearningA Robust Policy Bootstrapping Algorithm for Multi-objective Reinforcement Learning in Non-stationary Environments
Multi-objective Markov decision processes are a special kind of multi-objective optimization problem that involves sequential decision making while satisfying the Markov property of stochastic processes. Multi-objective …
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Intrinsically Motivated Hierarchical Policy Learning in Multi-objective Markov Decision Processes
Multi-objective Markov decision processes are sequential decision-making problems that involve multiple conflicting reward functions that cannot be optimized simultaneously without a compromise. This type of problems can…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering…
energy managementManagementMulti-Objective Reinforcement Learningreinforcement-learning+1