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In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process.
A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems
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Multi-objective reinforcement learning using sets of pareto dominating policies
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Computing convex coverage sets for faster multi-objective coordination
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Quality assessment of MORL algorithms: A utility-based approach. In Benelearn 2015: proceedings of the 24th annual machine learning conference of Belgium and the Netherlands
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Ammar Jalalimanesh, Hamidreza Shahabi Haghighi, Abbas Ahmadi, Hossein Hejazian, and Madjid Soltani. 2017 · 2017
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Manifold-based multi-objective policy search with sample reuse
Simone Parisi, Matteo Pirotta, and Jan Peters. 2017 · 2017
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Microscopic Traffic Simulation using SUMO, In The 21st IEEE International Conference on Intelligent Transportation Systems
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Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan. 2019 · 2019
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A practical guide to multi-objective reinforcement learning and planning
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