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Analysing Congestion Problems in Multi-agent Reinforcement Learning.

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Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games., , , , , and . CoRR, (2020)On nash equilibria in normal-form games with vectorial payoffs., , , and . Auton. Agents Multi Agent Syst., 36 (2): 53 (2022)Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey., , , and . AAMAS, page 2158-2160. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Bridging the Gap Between Single and Multi Objective Games., , , , and . AAMAS, page 224-232. ACM, (2023)Deep Multi-agent Reinforcement Learning in a Homogeneous Open Population., , , , and . BNCAI, volume 1021 of Communications in Computer and Information Science, page 90-105. Springer, (2018)Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning., , , , , , , , , and 2 other author(s). AAMAS, page 2717-2721. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)Analysing Congestion Problems in Multi-agent Reinforcement Learning., , and . AAMAS, page 1705-1707. ACM, (2017)Toll-based reinforcement learning for efficient equilibria in route choice., , , , and . Knowledge Eng. Review, (2020)A Brief Guide to Multi-Objective Reinforcement Learning and Planning., , , , , , , , , and 8 other author(s). AAMAS, page 1988-1990. ACM, (2023)Emergent Cooperation under Uncertain Incentive Alignment., , , and . AAMAS, page 1521-1530. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)