A practical guide to multi-objective reinforcement learning and planning

CF Hayes, R Rădulescu, E Bargiacchi… - Autonomous Agents and …, 2022 - Springer
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
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

GS Fischer, GO Ramos, CA Costa, AM Alberti… - IoT, 2024 - mdpi.com
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 …

MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning

F Felten, U Ucak, H Azmani, G Peng, W Röpke… - arxiv preprint arxiv …, 2024 - arxiv.org
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains
involve complex decision-making processes that must balance multiple conflicting …

Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects

JM da Silva, GO Ramos, JLV Barbosa - Algorithms, 2023 - mdpi.com
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 …

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 …

Decentralized multi-agent path finding framework and strategies based on automated negotiation

MO Keskin, F Cantürk, C Eran, R Aydoğan - Autonomous Agents and Multi …, 2024 - Springer
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 …

Toll-based reinforcement learning for efficient equilibria in route choice

GO Ramos, BC Da Silva, R Rădulescu… - The Knowledge …, 2020 - cambridge.org
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 …

[PDF][PDF] A brief guide to multi-objective reinforcement learning and planning

CF Hayes, R Rădulescu, E Bargiacchi… - Proceedings of the …, 2023 - southampton.ac.uk
Real-world sequential decision-making tasks are usually complex, and require trade-offs
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 …

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 …