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Interactively Learning the User's Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits.

, , , and . AAMAS, page 1611-1620. ACM, (2024)

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Multi-Objective Deep Reinforcement Learning., , , and . CoRR, (2016)AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)., , and . J. Mach. Learn. Res., (2020)Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds., , , , , and . CoRR, (2022)Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey., , , and . CoRR, (2019)Thompson Sampling for Factored Multi-Agent Bandits., , , , and . CoRR, (2019)Convex coverage set methods for multi-objective collaborative decision making.. AAMAS, page 1727-1728. IFAAMAS/ACM, (2014)A utility-based analysis of equilibria in multi-objective normal-form games., , , , and . Knowl. Eng. Rev., (2020)Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021)., , , , , , , , , and 2 other author(s). Auton. Agents Multi Agent Syst., 36 (2): 41 (2022)Thompson Sampling for m-top Exploration., , , , , and . BNAIC/BENELEARN, volume 2491 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Solving Transition-Independent Multi-Agent MDPs with Sparse Interactions., , , , and . AAAI, page 3174-3180. AAAI Press, (2016)