WAE-PCN: Wasserstein-autoencoded Pareto Conditioned Networks

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In real-world problems, decision makers often have to balance mul- tiple objectives, which can result in trade-offs. One approach to finding a compromise is to use a multi-objective approach, which builds a set of all optimal trade-offs called a Pareto front. Learning the Pareto front requires exploring many different parts of the state- space, which can be time-consuming and increase the chances of encountering undesired or dangerous parts of the state-space. In this preliminary work, we propose a method that combines two frameworks, Pareto Conditioned Networks (PCN) and Wasserstein auto-encoded MDPs (WAE-MDPs), to efficiently learn all possible trade-offs while providing formal guarantees on the learned poli- cies. The proposed method learns the Pareto-optimal policies while providing safety and performance guarantees, especially towards unexpected events, in the multi-objective setting.
Originele taal-2English
TitelProc. of the Adaptive and Learning Agents Workshop (ALA 2023)
RedacteurenFrancisco Cruz, Conor F. Hayes , Caroline Wang, Connor Yates
Plaats van productieLondon, UK
Aantal pagina's7
ISBN van elektronische versieNone
StatusPublished - 29 mei 2023
Evenement2023 Adaptive and Learning Agents Workshop at AAMAS - London, United Kingdom
Duur: 29 mei 202330 mei 2023


Workshop2023 Adaptive and Learning Agents Workshop at AAMAS
Verkorte titelALA 2023
Land/RegioUnited Kingdom
Internet adres

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