Papers Multi-Objective Reinforcement Learning
“Multi-Objective Reinforcement Learning” 태그가 달린 논문 143편 · 필터 해제
Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction
Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture calle…
Multi-Objective Reinforcement LearningTraffic Signal ControlScaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL
The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives. In practice, an agent's preferences over the objectives may not be known apriori,…
Decision MakingMuJoCoMulti-Objective Reinforcement LearningInferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the be…
Decision MakingMulti-Objective Reinforcement LearningLatent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that embody different patterns of compromises be…
Decision MakingDeep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learning+3Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing
We study a multi-objective multi-armed bandit problem in a dynamic environment. The problem portrays a decision-maker that sequentially selects an arm from a given set. If selected, each action produces a reward vector, …
Change DetectionMulti-Armed BanditsMulti-Objective Reinforcement LearningA Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis
Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multi-task problem is to establish a scalar objective functi…
Decision MakingMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+2Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of …
Active LearningMulti-Objective Reinforcement LearningLexicographic Multi-Objective Reinforcement Learning
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximi…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning
We consider the problem of two active particles in 2D complex flows with the multi-objective goals of minimizing both the dispersion rate and the energy consumption of the pair. We approach the problem by means of Multi …
Multi-Objective Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+1Elixir: A system to enhance data quality for multiple analytics on a video stream
IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, healthcare, safety and security, transportation, manu…
Multi-Objective Reinforcement LearningQ-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different prefere…
continuous-controlContinuous ControlMulti-Objective Reinforcement LearningWelfare and Fairness in Multi-objective Reinforcement Learning
We study fair multi-objective reinforcement learning in which an agent must learn a policy that simultaneously achieves high reward on multiple dimensions of a vector-valued reward. Motivated by the fair resource allocat…
FairnessMulti-Objective Reinforcement LearningQ-Learningreinforcement-learning+2MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments. It introduces a standardized API that facilitates conducting experiments and performance analyse…
Multi-Objective Reinforcement LearningOpenAI Gymreinforcement-learningReinforcement Learning+1Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from a single execution of a policy. In these settings, making decisions based on the average future returns is n…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning
Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) to solve multi-objective problems. One of common algorithm is to extend scalar value Q-lear…
Decision MakingMulti-Objective Reinforcement LearningQ-Learningreinforcement-learning+1Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL
Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group …
ManagementMulti-agent Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learningPD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
Multi-objective reinforcement learning (MORL) approaches have emerged to tackle many real-world problems with multiple conflicting objectives by maximizing a joint objective function weighted by a preference vector. Thes…
continuous-controlContinuous ControlMulti-Objective Reinforcement Learningreinforcement-learning+2Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example…
Multi-Objective Reinforcement LearningAnchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalariz…
FairnessMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Pareto Conditioned Networks
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with the number of objectives, and recovering a…
Multi-Objective Reinforcement Learning