WAE-PCN: Wasserstein-autoencoded Pareto Conditioned Networks

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Abstract

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.
Original languageEnglish
Title of host publicationProc. of the Adaptive and Learning Agents Workshop (ALA 2023)
EditorsFrancisco Cruz, Conor F. Hayes , Caroline Wang, Connor Yates
Place of PublicationLondon, UK
Pages1-7
Number of pages7
Volumehttps://alaworkshop2023.github.io/
Edition15
ISBN (Electronic)None
Publication statusPublished - 29 May 2023
Event2023 Adaptive and Learning Agents Workshop at AAMAS - London, United Kingdom
Duration: 29 May 202330 May 2023
https://alaworkshop2023.github.io

Workshop

Workshop2023 Adaptive and Learning Agents Workshop at AAMAS
Abbreviated titleALA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/2330/05/23
Internet address

Keywords

  • Multi-objective
  • Reinforcement Learning
  • Formal Methods
  • Representation Learning

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