Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use AI to provide efficient bicycle disp…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning
Many real-word decision or control problems involve multiple conflicting objectives and uncertainties, which requires learned policies are not only Pareto optimal but also robust. In this paper, we proposed a novel algor…
Bayesian OptimizationDiversityMulti-Objective Reinforcement Learningreinforcement-learning+2Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
In this paper we consider multi-objective reinforcement learning where the objectives are balanced using preferences. In practice, the preferences are often given in an adversarial manner, e.g., customers can be picky in…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Provable Multi-Objective Reinforcement Learning with Generative Models
Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple objectives exist without known relative c…
Multi-Objective Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments
Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the…
ManagementMulti-Objective Reinforcement LearningReinforcement Learning (RL)Collision Avoidance Robotics Via Meta-Learning (CARML)
This paper presents an approach to exploring a multi-objective reinforcement learning problem with Model-Agnostic Meta-Learning. The environment we used consists of a 2D vehicle equipped with a LIDAR sensor. The goal of …
Collision AvoidanceMeta-LearningMulti-Objective Reinforcement Learningreinforcement-learning+2A Distributional View on Multi-Objective Policy Optimization
Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical prefe…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)A distributional view on multi objective policy optimization
Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units with different scales, which can make it challenging for practitioners to express numeric…
Multi-Objective Reinforcement LearningReinforcement Learning (RL)Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Many real-world control problems involve conflicting objectives where we desire a dense and high-quality set of control policies that are optimal for different objective preferences (called Pareto-optimal). While extensi…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multiple times. The multi-objective reinforcem…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation
Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives. A typical approach to obt…
MuJoCoMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Solving single-objective tasks by preference multi-objective reinforcement learning
There ubiquitously exist many single-objective tasks in the real world that are inevitably related to some other objectives and influenced by them. We call such task as the objective-constrained task, which is inherently…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
A common approach for defining a reward function for Multi-objective Reinforcement Learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then treated as design parameters dependent on t…
Autonomous VehiclesMulti-Objective Reinforcement LearningReinforcement LearningReinforcement Learning (RL)A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple comp…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Meta-Learning for Multi-objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objective…
Computational Efficiencycontinuous-controlContinuous ControlDecision Making+6Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning …
Deep Reinforcement LearningMolecular Graph GenerationMulti-Objective Reinforcement LearningQ-Learning+3Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Autonomous cyber-physical agents and systems play an increasingly large role in our lives. To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques t…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time a…
Deep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1A Multi-Objective Deep Reinforcement Learning Framework
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy…
Deep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length. In this work, we propose a s…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1