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Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning.

, , , and . AAMAS, page 1756-1758. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)

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Pareto Conditioned Networks., , and . AAMAS, page 1110-1118. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning., , , , and . AAMAS, page 1530-1532. ACM, (2021)Near On-Policy Experience Sampling in Multi-Objective Reinforcement Learning., , , and . AAMAS, page 1756-1758. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Interactively Learning the User's Utility for Best-Arm Identification in Multi-Objective Multi-Armed Bandits., , , and . AAMAS, page 1611-1620. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)A Brief Guide to Multi-Objective Reinforcement Learning and Planning., , , , , , , , , and 8 other author(s). AAMAS, page 1988-1990. ACM, (2023)Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models., , , , , and . ECML/PKDD (3), volume 12459 of Lecture Notes in Computer Science, page 463-478. Springer, (2020)Actor-critic multi-objective reinforcement learning for non-linear utility functions., , , , and . Auton. Agents Multi Agent Syst., 37 (2): 23 (October 2023)A practical guide to multi-objective reinforcement learning and planning., , , , , , , , , and 8 other author(s). Auton. Agents Multi Agent Syst., 36 (1): 26 (2022)Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning., , , and . AAMAS, page 1524-1526. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs., , , and . Trans. Mach. Learn. Res., (2023)