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A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games.

, , and . AAMAS, page 1625-1627. ACM, (2017)

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A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games., , and . AAMAS, page 1625-1627. ACM, (2017)Know Your Enemy: Identifying Adversarial Behaviours in Deep Reinforcement Learning Agents (Student Abstract)., , and . AAAI, page 16176-16177. AAAI Press, (2023)A Brief Guide to Multi-Objective Reinforcement Learning and Planning., , , , , , , , , and 8 other author(s). AAMAS, page 1988-1990. ACM, (2023)Go-Explore for Residential Energy Management., , and . ECAI Workshops (2), volume 1948 of Communications in Computer and Information Science, page 133-139. Springer, (2023)Special issue on adaptive and learning agents 2018., , , and . Knowl. Eng. Rev., (2021)A practical guide to multi-objective reinforcement learning and planning., , , , , , , , , and 8 other author(s). Auton. Agents Multi Agent Syst., 36 (1): 26 (2022)Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making., , , , and . CoRR, (2021)Decision-Theoretic Planning for the Expected Scalarised Returns., , , and . AAMAS, page 1621-1623. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems., , , , , , and . VISIGRAPP (5: VISAPP), page 564-572. SciTePress, (2019)Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models., , , , and . CoRR, (2022)