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A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0
Smart cities can improve the quality of life of citizens by optimizing the utilization of
resources. In an IoT-connected environment, people's health can be constantly monitored …
resources. In an IoT-connected environment, people's health can be constantly monitored …
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains
involve complex decision-making processes that must balance multiple conflicting …
involve complex decision-making processes that must balance multiple conflicting …
Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects
The Shortest Path (SP) problem resembles a variety of real-world situations where one
needs to find paths between origins and destinations. A generalization of the SP is the …
needs to find paths between origins and destinations. A generalization of the SP is the …
Multi-objective reinforcement learning–concept, approaches and applications
L Zhang, Z Qi, Y Shi - Procedia Computer Science, 2023 - Elsevier
Real-world decision-making tasks are generally complicated and require trade-offs between
multiple, even conflicting, objectives. As the advent and great development of advanced …
multiple, even conflicting, objectives. As the advent and great development of advanced …
Decentralized multi-agent path finding framework and strategies based on automated negotiation
This paper introduces a negotiation framework to solve the Multi-Agent Path Finding (MAPF)
Problem for self-interested agents in a decentralized fashion. The framework aims to …
Problem for self-interested agents in a decentralized fashion. The framework aims to …
Toll-based reinforcement learning for efficient equilibria in route choice
The problem of traffic congestion incurs numerous social and economical repercussions and
has thus become a central issue in every major city in the world. For this work we look at the …
has thus become a central issue in every major city in the world. For this work we look at the …
[PDF][PDF] A brief guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are usually complex, and require trade-offs
between multiple–often conflicting–objectives. However, the majority of research in …
between multiple–often conflicting–objectives. However, the majority of research in …
The multi-objective dynamic shortest path problem
JM da Silva, GO Ramos… - 2022 IEEE Congress on …, 2022 - ieeexplore.ieee.org
Multi-objective decision-making and dynamic short-est paths are two areas of research
widely studied and of great importance for computer science, engineering, and economics …
widely studied and of great importance for computer science, engineering, and economics …
Designing High-Occupancy Toll Lanes: A Game-Theoretic Analysis
Z Zhang, R Yang, M Wu - arxiv preprint arxiv:2408.01413, 2024 - arxiv.org
In this article, we study the optimal design of High Occupancy Toll (HOT) lanes. The traffic
authority determines the road capacity allocation between HOT lanes and ordinary lanes, as …
authority determines the road capacity allocation between HOT lanes and ordinary lanes, as …